The document presents a study that uses machine learning approaches to predict diabetes for both typical and non-typical cases. Three machine learning algorithms (Bagging, Logistic Regression, Random Forest) were applied to a dataset of 340 patients with 26 features, and their accuracy was measured. Random Forest performed best with an accuracy of 90.29%, followed by Bagging at 89.12% and Logistic Regression at 83.24%.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET Journal
This document presents research on using machine learning algorithms to diagnose diabetes. The researchers collected a dataset of 15,000 patient records from the National Institute of Diabetes and Digestive and Kidney Diseases. They analyzed the dataset and used machine learning algorithms like decision trees, naive Bayes, support vector machines, and k-nearest neighbors to build predictive models. The models were evaluated based on accuracy and other performance metrics. The naive Bayes classifier achieved the highest accuracy of 72% in predicting whether patients had diabetes. The research aims to develop a machine learning system that can predict diabetes early to help treat patients before the disease becomes critical.
DIABETES PREDICTOR USING ENSEMBLE TECHNIQUEIRJET Journal
This document describes a study that developed an ensemble machine learning model to predict diabetes using the Pima Indian Diabetes dataset. The study used various machine learning algorithms like decision trees, random forest, SVM, and multilayer perceptron. It then proposed weighting and integrating the outputs of these models to improve diabetes prediction performance, where weights were calculated based on each model's AUC, F1 score, accuracy, and recall on the classification task. The models were evaluated using cross-validation on the Pima Indian Diabetes dataset under the same parameter settings. Previous literature that used machine learning techniques for diabetes prediction is also reviewed.
This document discusses using machine learning techniques to predict diabetes. Specifically:
- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
This document describes a study that uses machine learning techniques to predict heart disease and diabetes from medical data. The study collected data from a public repository and preprocessed it to handle missing values. Feature selection was performed using chi-square and principal component analysis to identify important features. Three boosting classifiers - Adaptive boosting, Gradient boosting, and Extreme Gradient boosting - were trained on the data and evaluated based on accuracy. The results showed that the boosting classifiers achieved accurate prediction for both heart disease and diabetes, with the highest accuracy reported for specific classifiers and diseases.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
This paper proposes a neural network model to predict diabetes using the Pima Indian Diabetes dataset. The paper preprocesses the data by handling outliers and missing values. It then performs feature selection and uses ADASYN oversampling to address class imbalance before training a multilayer perceptron classifier. Experimental results show the proposed model achieves 84% accuracy, outperforming other models like SVM and random forest. The paper concludes the model is effective for diabetes prediction but could be extended to other diseases.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET Journal
This document presents research on using machine learning algorithms to diagnose diabetes. The researchers collected a dataset of 15,000 patient records from the National Institute of Diabetes and Digestive and Kidney Diseases. They analyzed the dataset and used machine learning algorithms like decision trees, naive Bayes, support vector machines, and k-nearest neighbors to build predictive models. The models were evaluated based on accuracy and other performance metrics. The naive Bayes classifier achieved the highest accuracy of 72% in predicting whether patients had diabetes. The research aims to develop a machine learning system that can predict diabetes early to help treat patients before the disease becomes critical.
DIABETES PREDICTOR USING ENSEMBLE TECHNIQUEIRJET Journal
This document describes a study that developed an ensemble machine learning model to predict diabetes using the Pima Indian Diabetes dataset. The study used various machine learning algorithms like decision trees, random forest, SVM, and multilayer perceptron. It then proposed weighting and integrating the outputs of these models to improve diabetes prediction performance, where weights were calculated based on each model's AUC, F1 score, accuracy, and recall on the classification task. The models were evaluated using cross-validation on the Pima Indian Diabetes dataset under the same parameter settings. Previous literature that used machine learning techniques for diabetes prediction is also reviewed.
This document discusses using machine learning techniques to predict diabetes. Specifically:
- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
This document describes a study that uses machine learning techniques to predict heart disease and diabetes from medical data. The study collected data from a public repository and preprocessed it to handle missing values. Feature selection was performed using chi-square and principal component analysis to identify important features. Three boosting classifiers - Adaptive boosting, Gradient boosting, and Extreme Gradient boosting - were trained on the data and evaluated based on accuracy. The results showed that the boosting classifiers achieved accurate prediction for both heart disease and diabetes, with the highest accuracy reported for specific classifiers and diseases.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
This paper proposes a neural network model to predict diabetes using the Pima Indian Diabetes dataset. The paper preprocesses the data by handling outliers and missing values. It then performs feature selection and uses ADASYN oversampling to address class imbalance before training a multilayer perceptron classifier. Experimental results show the proposed model achieves 84% accuracy, outperforming other models like SVM and random forest. The paper concludes the model is effective for diabetes prediction but could be extended to other diseases.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
An automatic heart disease prediction using cluster-based bidirectional LSTM ...BASMAJUMAASALEHALMOH
The document discusses a proposed method called cluster-based bi-directional LSTM (C-BiLSTM) for predicting heart disease using medical data. C-BiLSTM uses K-means clustering on two datasets - a UCI heart disease dataset and a real-time dataset - to remove duplicate data before predicting heart disease using a bi-directional LSTM approach. The method achieved accuracy of 94.78% on the UCI dataset and 92.84% on the real-time dataset, outperforming other conventional classification methods like regression trees, SVM, logistic regression, KNN and GRU. The authors believe C-BiLSTM provides better heart disease prediction by analyzing data bidirectionally and linear relationships between features.
This document describes a study that developed an Android application to predict and suggest measures for diabetes using data mining techniques. The study used the Pima Indian diabetes dataset to build a decision tree classification model using the C4.5 algorithm to predict whether a person is diabetic or not based on their attributes. The most significant attributes identified for prediction were plasma glucose level, body mass index, diabetes pedigree function, and insulin level. The developed Android app allows a user to enter their details, which are then run through the decision tree model to predict their diabetes status and provide suggested measures if predicted to be diabetic. The goal of the study was to create a mobile application to help individuals assess their risk of diabetes and maintain healthy habits.
Performance Evaluation of Data Mining Algorithm on Electronic Health Record o...BRNSSPublicationHubI
This document discusses the performance evaluation of various data mining algorithms on an electronic health record database of diabetic patients. It first provides background on data mining and its applications in healthcare, particularly for diabetes. It then describes the methodology used, which involved preprocessing the data and applying several classification algorithms (decision stump, J48, random forest, neural network, Zero R, One R) to predict diabetes status. The results of each algorithm are evaluated based on accuracy, precision, recall, and error rate. Overall, the document aims to compare the performance of these algorithms on an electronic health record database for diabetes prediction.
A hybrid model for heart disease prediction using recurrent neural network an...BASMAJUMAASALEHALMOH
This document presents research on developing a hybrid deep learning model using recurrent neural networks (RNN) and long short-term memory (LSTM) to predict heart disease. The researchers created a model that classifies synthetic cardiac data using different RNN and LSTM approaches with cross-validation. They evaluated the system's performance using various machine learning methods and found that the deep hybrid learning approach was more accurate than either classic deep learning or machine learning alone. The document provides background on heart disease and motivation for developing a more accurate predictive model, describes the methodology used including the dataset, and outlines the experimental setup and algorithm.
Optimized stacking ensemble for early-stage diabetes mellitus predictionIJECEIAES
This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.
Hybrid prediction model with missing value imputation for medical data 2015-g...Jitender Grover
The document presents a novel hybrid prediction model called HPM-MI that uses K-means clustering and multilayer perceptron (MLP) to improve predictive classification for medical data with missing values. The model first analyzes 11 different imputation techniques using K-means clustering to select the best one for filling missing values in the data. It then uses K-means clustering again to validate class labels and remove incorrectly classified instances before applying the MLP classifier. The model is tested on three medical datasets from the UCI repository and shows improved accuracy, sensitivity, specificity and other metrics compared to existing methods, particularly when datasets have large numbers of missing values.
Multivariate sample similarity measure for feature selection with a resemblan...IJECEIAES
Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
This document proposes a hybrid machine learning model to predict heart disease. It uses a dataset from UCI with 303 instances and 14 attributes on factors like age, sex, cholesterol levels, etc. It compares the performance of decision tree, random forest, and a hybrid model combining the two. The hybrid model achieves the best accuracy of 88.7% for heart disease prediction. It discusses implementing the models in Python using libraries like sklearn. The results show random forest and hybrid models more accurately detect cardiovascular issues compared to decision trees alone. The proposed hybrid model aims to improve uniqueness and optimization for heart disease risk prediction.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
Improving the performance of k nearest neighbor algorithm for the classificat...IAEME Publication
The document discusses improving the performance of the k-nearest neighbor (kNN) algorithm for classifying diabetes datasets with missing values. It first provides background on diabetes and challenges with missing data. It then describes various data preprocessing techniques used to handle missing values, including mean imputation. The document outlines the kNN classification algorithm and metrics like accuracy and error rate to evaluate performance. It applies these techniques to the Pima Indian diabetes dataset and finds that imputing missing values along with suitable preprocessing like normalization increases classification accuracy compared to ignoring missing values or imputation alone.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm...IRJET Journal
This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
An efficient stacking based NSGA-II approach for predicting type 2 diabetesIJECEIAES
Diabetes has been acknowledged as a well-known risk factor for renal and cardiovascular disorders, cardiac stroke and leads to a lot of morbidity in the society. Reducing the disease prevalence in the community will provide substantial benefits to the community and lessen the burden on the public health care system. So far, to detect the disease innumerable data mining approaches have been used. These days, incorporation of machine learning is conducive for the construction of a faster, accurate and reliable model. Several methods based on ensemble classifiers are being used by researchers for the prediction of diabetes. The proposed framework of prediction of diabetes mellitus employs an approach called stacking based ensemble using non-dominated sorting genetic algorithm (NSGA-II) scheme. The primary objective of the work is to develop a more accurate prediction model that reduces the lead time i.e., the time between the onset of diabetes and clinical diagnosis. Proposed NSGA-II stacking approach has been compared with Boosting, Bagging, Random Forest and Random Subspace method. The performance of Stacking approach has eclipsed the other conventional ensemble methods. It has been noted that k-nearest neighbors (KNN) gives a better performance over decision tree as a stacking combiner.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
DIAGNOSIS OF RHEUMATOID ARTHRITIS USING AN ENSEMBLE LEARNING APPROACH cscpconf
Rheumatoid arthritis is one of the diseases that its cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnosis and treatment of the disease. In this
study, a predictive model is suggested that diagnoses rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients referred to rheumatology clinic. For each
patient a record consists of several clinical and demographic features is saved. After data
analysis and pre-processing operations, three different methods are combined to choose proper
features among all the features. Various data classification algorithms were applied on these
features. Among these algorithms Adaboost had the highest precision. In this paper, we
proposed a new classification algorithm entitled CS-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboost algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of Adaboost in predicting of Rheumatoid
Arthritis.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
002 Essay Example Refle. Online assignment writing service.Scott Faria
The document provides instructions for creating an account and submitting an assignment request on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete a form with assignment details and deadline. 3) Writers will bid on the request and the customer can choose a writer. 4) The customer receives the paper and can request revisions if needed. 5) HelpWriting.net guarantees original, high-quality content and refunds are offered for plagiarized work.
How To Write A Proper Observation Essay - AdairScott Faria
The document provides instructions for seeking writing help from HelpWriting.net. It outlines a 5-step process: 1) Create an account, 2) Complete an order form providing instructions and deadline, 3) Review bids from writers and select one, 4) Review the completed paper and authorize payment, 5) Request revisions until satisfied. The service aims to provide original, high-quality content and offers refunds for plagiarized work.
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Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
An automatic heart disease prediction using cluster-based bidirectional LSTM ...BASMAJUMAASALEHALMOH
The document discusses a proposed method called cluster-based bi-directional LSTM (C-BiLSTM) for predicting heart disease using medical data. C-BiLSTM uses K-means clustering on two datasets - a UCI heart disease dataset and a real-time dataset - to remove duplicate data before predicting heart disease using a bi-directional LSTM approach. The method achieved accuracy of 94.78% on the UCI dataset and 92.84% on the real-time dataset, outperforming other conventional classification methods like regression trees, SVM, logistic regression, KNN and GRU. The authors believe C-BiLSTM provides better heart disease prediction by analyzing data bidirectionally and linear relationships between features.
This document describes a study that developed an Android application to predict and suggest measures for diabetes using data mining techniques. The study used the Pima Indian diabetes dataset to build a decision tree classification model using the C4.5 algorithm to predict whether a person is diabetic or not based on their attributes. The most significant attributes identified for prediction were plasma glucose level, body mass index, diabetes pedigree function, and insulin level. The developed Android app allows a user to enter their details, which are then run through the decision tree model to predict their diabetes status and provide suggested measures if predicted to be diabetic. The goal of the study was to create a mobile application to help individuals assess their risk of diabetes and maintain healthy habits.
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This document discusses the performance evaluation of various data mining algorithms on an electronic health record database of diabetic patients. It first provides background on data mining and its applications in healthcare, particularly for diabetes. It then describes the methodology used, which involved preprocessing the data and applying several classification algorithms (decision stump, J48, random forest, neural network, Zero R, One R) to predict diabetes status. The results of each algorithm are evaluated based on accuracy, precision, recall, and error rate. Overall, the document aims to compare the performance of these algorithms on an electronic health record database for diabetes prediction.
A hybrid model for heart disease prediction using recurrent neural network an...BASMAJUMAASALEHALMOH
This document presents research on developing a hybrid deep learning model using recurrent neural networks (RNN) and long short-term memory (LSTM) to predict heart disease. The researchers created a model that classifies synthetic cardiac data using different RNN and LSTM approaches with cross-validation. They evaluated the system's performance using various machine learning methods and found that the deep hybrid learning approach was more accurate than either classic deep learning or machine learning alone. The document provides background on heart disease and motivation for developing a more accurate predictive model, describes the methodology used including the dataset, and outlines the experimental setup and algorithm.
Optimized stacking ensemble for early-stage diabetes mellitus predictionIJECEIAES
This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.
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Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
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rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
This document proposes a hybrid machine learning model to predict heart disease. It uses a dataset from UCI with 303 instances and 14 attributes on factors like age, sex, cholesterol levels, etc. It compares the performance of decision tree, random forest, and a hybrid model combining the two. The hybrid model achieves the best accuracy of 88.7% for heart disease prediction. It discusses implementing the models in Python using libraries like sklearn. The results show random forest and hybrid models more accurately detect cardiovascular issues compared to decision trees alone. The proposed hybrid model aims to improve uniqueness and optimization for heart disease risk prediction.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
Improving the performance of k nearest neighbor algorithm for the classificat...IAEME Publication
The document discusses improving the performance of the k-nearest neighbor (kNN) algorithm for classifying diabetes datasets with missing values. It first provides background on diabetes and challenges with missing data. It then describes various data preprocessing techniques used to handle missing values, including mean imputation. The document outlines the kNN classification algorithm and metrics like accuracy and error rate to evaluate performance. It applies these techniques to the Pima Indian diabetes dataset and finds that imputing missing values along with suitable preprocessing like normalization increases classification accuracy compared to ignoring missing values or imputation alone.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm...IRJET Journal
This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
An efficient stacking based NSGA-II approach for predicting type 2 diabetesIJECEIAES
Diabetes has been acknowledged as a well-known risk factor for renal and cardiovascular disorders, cardiac stroke and leads to a lot of morbidity in the society. Reducing the disease prevalence in the community will provide substantial benefits to the community and lessen the burden on the public health care system. So far, to detect the disease innumerable data mining approaches have been used. These days, incorporation of machine learning is conducive for the construction of a faster, accurate and reliable model. Several methods based on ensemble classifiers are being used by researchers for the prediction of diabetes. The proposed framework of prediction of diabetes mellitus employs an approach called stacking based ensemble using non-dominated sorting genetic algorithm (NSGA-II) scheme. The primary objective of the work is to develop a more accurate prediction model that reduces the lead time i.e., the time between the onset of diabetes and clinical diagnosis. Proposed NSGA-II stacking approach has been compared with Boosting, Bagging, Random Forest and Random Subspace method. The performance of Stacking approach has eclipsed the other conventional ensemble methods. It has been noted that k-nearest neighbors (KNN) gives a better performance over decision tree as a stacking combiner.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
DIAGNOSIS OF RHEUMATOID ARTHRITIS USING AN ENSEMBLE LEARNING APPROACH cscpconf
Rheumatoid arthritis is one of the diseases that its cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnosis and treatment of the disease. In this
study, a predictive model is suggested that diagnoses rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients referred to rheumatology clinic. For each
patient a record consists of several clinical and demographic features is saved. After data
analysis and pre-processing operations, three different methods are combined to choose proper
features among all the features. Various data classification algorithms were applied on these
features. Among these algorithms Adaboost had the highest precision. In this paper, we
proposed a new classification algorithm entitled CS-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboost algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of Adaboost in predicting of Rheumatoid
Arthritis.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
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An Empirical Study On Diabetes Mellitus Prediction For Typical And Non-Typical Cases Using Machine Learning Approaches
1. An Empirical Study on Diabetes Mellitus Prediction
for Typical and Non-Typical Cases using Machine
Learning Approaches
Md. Tanvir Islam1
, M. Raihan2
, Fahmida Farzana3
, Md. Golam Morshed Raju4
and Md. Bellal Hossain5
Department of Computer Science and Engineering, North Western University, Khulna, Bangladesh1-4
Electronics and Communication Engineering Discipline, Khulna University, Khulna, Bangladesh5
Emails: tanvirislamnwu@gmail.com1
, mraihan@ieee.org2
, mraihan@nwu.edu.bd2
, raihanbme@gmail.com2
,
tanni.dorsonindrio@gmail.com3
, golam.morshed.raju.cse01@gmail.com4
and md.bellal.ku@gmail.com5
Abstract—Diabetes is a non-communicable disease and in-
creasing at an alarming rate all over the world. Having a high
sugar level in blood or lack of insulin are the primary reasons.
So, it is important to find an effective way to predict diabetes
before it turns into a major problem for human health. It is
possible to take control of diabetes on an early stage if we
take precautions. For this study, we have collected 340 instances
with 26 features of patients who have already diabetes with
various symptoms categorized by two types Typical and Non-
Typical. For training the dataset, cross-validation technique has
been used and for classification, three Machine Learning (ML)
algorithms such as Bagging, Logistic Regression and Random
Forest have been used. The accuracy for Bagging 89.12%, for
Logistic Regression 83.24% and for Random Forest 90.29%
which are very appreciative.
Keywords—Diabetes Mellitus, Type-2, Machine Learning, Bag-
ging, Logistic Regression, Random Forest, Typical, Non-typical.
I. INTRODUCTION
Nowadays in the era of modern technology, many people
are suffering from numerous diseases and diabetes is one of
them. Diabetes Mellitus (DM) arises when the level of sugar is
too high in our blood though it's our primary source of energy
[1]. In 2011, about 8.4 million or 10% of total population
have diabetes and according to the International Diabetes
Federation (IDF) in Bangladesh and the prevalence of diabetes
among adults will be increased to 13% by 2030 [2]. A news
report of Science Daily states that, If it's possible to prevent
diabetes on an earlier stage then possibility of minimizing the
devastating effects of diabetes is more [3]. Machine Learning
Techniques (MLT) are broadly utilized in medicinal forecasts
[4]. For example, a prediction model developed to predict
Type 2 Diabetes (T2D) using K-means and Decision Tree.
The accuracy, specificity, and sensitivity of the proposed model
are respectively 90.04% 91.28% and 87.27% [5]. For different
algorithms the percentage of accuracy can be different. So, it's
easy to know which algorithm is best from the current available
algorithms.
In this analysis, our goal is to identify the accuracy of
three popular algorithms named Bagging (BAG), Logistic
Regression (LR) and Random Forest (RF) by analyzing the
dataset and compare their performance and to integrate these
techniques in a system such as mobile or web to develop an
expert system.
The other part of the manuscript is arranged as follows:
in section II, section III the related works and methodology
have been elaborated with a distinguishing destination to the
justness of the classier algorithms respectively. In section
IV the outcome of this analysis has been clarified with the
impulsion to justify the novelty of this exploration work.
Finally, this research paper is terminated with section V.
II. RELATED WORKS
Deeraj et al. 2017 in [6] have proposed to use Bayesian
and K-Nearest Neighbor algorithms to predict diabetes malady
using data mining. The results of the prediction will depend
on the taken attributes for example age, pregnancy, tri fold
thick, BMI, bp function, bp diastolic and so on. Another
research performed on three available datasets to compare
the behavior of perceptron algorithms and they found that
the proposed algorithm is better than a perceptron algorithm
[7]. By using K-nearest neighbor (KNN) and Artificial Neural
Network (ANN) classify algorithms, another research team
estimated classifiers of diabetes diseases. They have used total
768 instances with 9 attributes for this evaluation. The accuracy
of ANN and KNN was 80.86% and 77.24% [8]. With Deep
Neural Network (DNN) and Support Vector Machine (SVM) a
system was developed to predict the diabetes. They have taken
total 8 significant attributes of patients such as Age, Number
of Times Pregnant, Plasma Glucose Concentration, Diastolic
Blood Pressure, Body Mass Index etc and got 77.86% accuracy
[9]. A mobile-based decision support system was developed
for gestational Diabetes Mellitus (DM) [10]. Another diabetes
prediction system was developed using cloud analytic where
they have used 3075 distinct person record with 14 variables
of all age group who may or may not diagnosed with Dia-
betes. They found that Logistic Regression outperformed than
Random Forest in all cases and that's why they determined to
use LR as their model [11]. Deepika Verma and Nidhi Mishra
conduct a study to identify Diabetes by using a dataset on
Naive Bayes (NB), J48, SMO, MLP, and REP Tree algorithms
and found that SMO gives 76.80% accuracy on diabetes dataset
[14].
Since diabetes is increasing at an alarming rate and it has
became one of the major health issues for human being, we
have felt the importance of finding out effective and efficient
solution. From this this perspective view, we have been moti-
IEEE - 45670
10th ICCCNT 2019
July 6-8, 2019 , IIT - Kanpur,
Kanpur, India
2. vated to increase the accuracy of Machine Learning algorithms
by using clinical dataset of diabetes affected patients.
III. METHODOLOGY
We can separate our strategy into four primary segments
as follows:
• Data Collection
• Data Preprocessing
• Data Training
• Applications of Machine Learning Algorithms
An overall work flow of our study has been shown in Fig. 1.
Import dataset with 340
instances and 27 features
Start
Data Preprocessing
Replace missing data with
Mean, Median and Mode
Feature Selection
Apply Best First Search
and Ranker Algorithm
Find Most
Significant Features
Apply 10-Fold
Cross-validation technique
Apply
Classification Algorithms
Bagging
Random
forest
Logistic
Regression
Determine
Statistical Matrics
Compare Performance
End
Fig. 1: Work-flow of the Overall Analysis
A. Data Collection:
For this analysis, we have collected data from Khulna
Diabetes Hospital, Khulna. The dataset includes total 340
TABLE I: Types and Names of Symptoms
Types of Symptoms Names of Symptoms
Typical
Thirst
Hunger
Weight Loss
Weakness
Non-Typical
Headache for High Blood Pressure
Burning Extremities
Weakness
instances with having 27 significant features for each instance.
The dataset contains basic information of patients and two
types of symptoms: Typical and Non-Typical. The Table I helps
to understand the categories of the symptoms.
B. Data preprocessing:
To handle missing information we've used two popular and
useful functions in WEKA 3.8 ( Waikato Environment for
Knowledge Analysis) . First, ReplaceMissingValue function
has been used to replace missing data. This function swaps
every single missing information for nominal and numeric
attributes with the modes and means [13]. We've used another
function named Randomize which can fill-up the missing field
without sacrificing too much performance [13].
C. Data Training:
For training all the features of the dataset shown in Table
II, we have used 10-Fold Cross-Validation technique. It is
a re-sampling technique to evaluate predictive models by
partitioning the original sample into a training set to train the
model, and a test set to evaluate it [14]. The methodology has
a solitary parameter considered K that alludes to the number
of algorithms that a given information test is to be part into. It
shuffles the dataset randomly, splits dataset into 10 groups and
finally abridge the expertise of the model utilizing the example
of model assessment scores [15].
D. Applications of ML Algorithms:
After having the preprocessed and trained dataset we have
applied three algorithms on the dataset. They are: Bagging,
Logistic Regression and Random Forest.
1) Bagging (BAG): It is a procurement method that re-
samples the preparation information to make new models
for each example that is drawn [16]. It makes a troupe of
arrangement models for a learning plan where each model
gives a similarly weighted forecast [14].
Input:
• R, a set of h training tuples
• t, the number of models in the ensemble
• A classification learning scheme (Decision Tree Algo-
rithm, Naive Bayesian, etc.)
Output: The ensemble, an associated model, L.
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3. TABLE II: Features List
Features Subcategory
Data
Distribution
Age
Lowest: 22 Mean ± SD
Highest: 80 49.21 ± 11.95
Sex
Male 47.65%
Female 52.35%
Profession
Lowest: Supervisor, Unemployed,
Servant, Banker
1.18%
Highest: Housewife 52.65%
Rest 46.17%
Height
Lowest: 133 cm
158.58 ± 8.40
Highest: 188 cm
Weight
Lowest: 33 kg
62.65 ± 11.03
Highest: 105 kg
Body Mass Index
Lowest: 11.6 Kgm-2
24.81 ± 3.53
Highest: 52.1Kgm-2
Heart Rate
Lowest: 56 bpm
74.94 ± 6.11
Highest: 90 bpm
Systolic Blood Pressure
Lowest: 90 mmHg
129.1 ± 12.39
Highest: 200 mmHg
Diastolic Blood Pressure
Lowest: 40 mmHg
81.52 ± 7.16
Highest: 110 mmHg
Blood Sugar Before
Meal
Lowest: 4.2 mmol/L
12.91 ± 4.83
Highest: 45.5 mmol/L
Blood Sugar After
Meal
Lowest: 4.4 mmol/L
18.51 ± 5.42
Highest: 45.8 mmol/L
Urine Color Before
Meal
Lowest: Orange 3.24%
Highest: Green 68.82%
Rest 27.94%
Urine Color After Meal
Lowest: Cyan 0.29%
Highest: Green 65.00%
Rest 34.71%
Drug History
Yes 99.41%
No 0.59%
Weight Loss
Yes 78.24%
No 21.76%
Thirst
Yes 92.35%
No 7.65%
Hunger
Yes 78.24%
No 21.76%
Relatives
Yes 79.41%
No 20.59%
Physical Activity
Yes 97.64%
No 2.36%
Smoking
Yes 6.18%
No 94.82%
Tobacco Chewing
Yes 12.9%
No 87.1%
Headache for Hight BP
Yes 81.18%
No 18.82%
Burning Extremities
Yes 82.94%
No 17.06%
Weakness
Yes 95.88%
No 4.12%
Symptoms Duration
Lowest: 1 Day Mean: 264.25
SD: 292.437
Highest: 3650 Days
Diabetes Mellitus
Yes 99.4%
No 0.59%
Outcome
Typical 61.18%
Non-Typical 14.70%
Both 24.12%
*SD = Standard Deviation
2) Logistic Regression (LR): LR is a well-known sys-
tem for grouping individuals into two totally unrelated and
thorough classifications, for instance, buyer, non-buyer and
responder, non-responder [17]. It predicts by the logit function
occurrence probabilistic outcome by fitting data of an event
[17].
LR accounts the logit of L, a log of the odds of a single
belonging to class 1 and can except much of a stretch be
changed over into the likelihood of an individual having a
place with class 1 [17].
The equations of Logit and Probability are as follow:
Logit L = a0 + a1 × Y1 + a2 × Y2 + · · · + an × Yn ...eq.(1)
Prop (L = 1) = exp
Logit L
1 + exp (Logit l)
...eq.(2)
An individual's predicted probability of belonging to class
1 is accounted by “plugging in” the values of the predictor
variables for that individual in the given two equations. Here,
a's = LR coefficients, which are determined by the calculus-
based method of maximum likelihood and it has no predictor
variable with which it is multiplied [17].
3) Random Forest (RF): RF is another ensemble technique
that used as classifiers. It also capable of performing regression
tasks [14]. If a training set, Y, of y tuples is given then the
procedure of begetting k decision trees is as follows: For each
iteration, j (j = 1, 2, ..., t), a training set, Yj, of y tuples is
sampled with replacement from Y. Let U be the number of
attributes to be used to determine the split at each node (where
U is much smaller than the number of available attributes).To
construct a decision tree classifier, Nj, randomly select, at each
node, U attributes as candidates for the split at the node [14].
IV. OUTCOMES
The result is analyzed based on 22 performance parameters
for example:
A. Seed:
It specify changing number randomly and getting different
result.
B. Correctly Classified Instances (CCI):
The accuracy of the model on the data used for testing
[13].
Accuracy =
Tp + Tn
Tp + Tn + Fp + Fn
...eq.(3)
Here, Tp= True positive, Tn= True negative, Fp= False positive,
Fn= False negative.
C. Kappa Statistics (KS):
The Kappa measurement is utilized to quantify the asser-
tion among anticipated and watched arrangements of a dataset.
[13].
K =
R0 − Re
1 − Re
...eq.(4)
Where, R0 = Relative watched understanding among raters
and Re = Theoretical likelihood of chance assertion.
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4. Fig. 2: ROC Curve of Typical Class for (a) Bagging at seed 3
(b) Logistic Regression at seed 3 (c) Random Forest at seed 1
D. KB Information Score:
For a correct class of an instance B we can consider 3
different cases [18] as follows:
• If P'(B) P(B) then score is positive
• If P'(B) P(B) then score is negative
• P'(B) = P(B) then no information (score 0)
where, P(B) = Probability of C, If P'(B) = Subordinate prob-
ability return by the classifier.
E. Mean Absolute Error (MAE):
It normal the size of the individual mistakes without
assessing their sign [13].
MAE =
| p1 − b1 | +...+ | pn − bn |
n
...eq.(5)
Here, p is for predicted value and b is for actual value.
Fig. 3: ROC Curve of Non-Typical Class for (a) Bagging at
seed 3 (b) Logistic Regression at seed 3 (c) Random Forest
at seed 1
F. Relative Absolute Error (RAE):
It is the total absolute error with the same kind of normal-
ization [13].
RAE =
| p1 − b1 | + · · · + | pn − bn |
| b1 − b̄ | + · · · + | bn − b̄ |
...eq.(6)
G. Specificity/ TN Rate:
It suggests the degree of people without infection who have
an adverse test result [13].
Specificity =
Tn
Fp + Tn
...eq.(7)
H. Precision (PRE):
Scientists characterize its by [13].
PRE =
Tp
Tp + Fp
...eq.(8)
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5. Fig. 4: ROC Curve of Both Class for (a) Bagging at seed 3
(b) Logistic Regression at seed 3 (c) Random Forest at seed 1
I. Recall (REC):
Researchers defined this parameter as follows [13],
REC =
Tp
Tp + Fn
...eq.(9)
J. F-Measure:
If it denotes by FM then,
FM = 2 ×
PRE × REC
PRE + REC
...eq.(10)
=
2 × Tp
2 × Tp + Fp + Fn
...eq.(11)
K. MCC:
The cohesion between PRE and REC [13].
TABLE III: Comparison of Statistical Metrics for SEED 1
Evaluation Metrics
Machine Learning Algorithms
Bagging Logistic
Random
Forest
Correctly Classified Instances 86.77% 80.88% 90.29%
Incorrectly Classified Instances 13.24% 19.12% 9.71%
Kappa statistic 0.75 0.65 0.81
KB Information Score 298.03 bits 279.89 bits 317.38 bits
Class complexity — order 0 454.75 bits 454.75 bits 454.75 bits
Class complexity — scheme 186.61 bits 8551.65 bits 148.78 bits
Mean absolute error 0.15 0.16 0.13
Root mean squared error 0.26 0.33 0.23
Relative absolute error 41.11% 43.05% 35.30%
Root relative squared error 60.31% 77.54% 54.31%
Coverage of cases
(0.95 level)
186.61 bits 8551.65 bits 148.78 bits
Mean rel. region size
(0.95 level)
0.15 0.16 0.13
Specificity/TN Rate
(Weighted Avg.)
0.26 0.33 0.23
Precision
(Weighted Avg.)
41.11% 43.05% 35.30%
Recall
(Weighted Avg.)
60.31% 77.54% 54.31%
F-Measure
(Weighted Avg.)
0.87 0.81 0.90
MCC
(Weighted Avg.)
0.75 0.64 0.82
ROC Area
(Weighted Avg.)
0.94 0.85 0.97
PRC Area
(Weighted Avg.)
0.93 0.78 0.96
L. ROC Area:
It is the probability that a randomly chosen positive in-
stance in the test data is ranked above a randomly chosen
negative instance, based on the ranking produced by the
classifier [13].
M. PRC Area:
It is an elective summary measurement that is favored by
a few specialists, especially in the data recovery zone [13].
N. Explanation of the Analysis:
The analysis has been accomplished in 3 seeds for each
algorithm for 3 classes named typical, non-typical and both.
Fig. 2 is only showing the best performance curves for class
Typical. For class typical BAG and LR have given the best per-
formance in seed 3 and accuracy was 89.1176% and 83.2353%
respectively. In seed number 1, RF performed better than seed
1 or 2 for the same class while the accuracy is 90.2941% which
is very impressive.
In Fig. 3, the scenario is not much different for class Non-
Typical than the Fig. 2. The BAG and LR again performed
well in seed 3 and similarly RF in seed 1 but keep in mind
that this time the class is Non-Typical. Moreover, in Fig. 4 the
results are again similar to Fig. 2 3. The BAG and LR again
given the best performance in seed 3 for class Both.
Table III shows the performance parameters and results of
our study where CCI are 86.7647%, 80.8824% 90.2941%
for BAG, LR RF respectively and the values of their KS are
0.753, 0.6512 0.8126. Values of Specificity are 0.62, 0.881
0.923 while the values of PRE are 0.866, 0.809 0.908.
In addition, the outcomes of ROC and PRC Area are 0.944,
0.847, 0.972 and 0.930, 0.775, 0.963 respectively.
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6. TABLE IV: Comparison of Statistical Metrics for SEED 2
Evaluation Metrics
Machine Learning Algorithms
Bagging Logistic
Random
Forest
Correctly Classified Instances 87.65% 80.29% 88.82%
Incorrectly Classified Instances 12.35% 19.71% 11.18%
Kappa statistic 0.76 0.64 0.78
KB Information Score 320.91 bits 263.70 bits 313.21 bits
Class complexity — order 0 454.75 bits 454.75 bits 454.75 bits
Class complexity — scheme 146.77 bits 1550.27 bits 155.03 bits
Mean absolute error 0.12 0.17 0.13
Root mean squared error 0.23 0.35 0.24
Relative absolute error 34.05% 47.06% 36.25%
Root relative squared error 54.37% 82.03% 55.72%
Coverage of cases
(0.95 level)
99.41% 87.06% 99.41%
Mean rel. region size
(0.95 level)
59.71% 49.61% 62.84%
Specificity/TN Rate
(Weighted Avg.)
0.87 0.81 0.90
Precision
(Weighted Avg.)
0.88 0.80 0.90
Recall
(Weighted Avg.)
0.88 0.80 0.89
F-Measure
(Weighted Avg.)
0.87 0.80 0.89
MCC
(Weighted Avg.)
0.76 0.63 0.79
ROC Area
(Weighted Avg.)
0.97 0.82 0.97
PRC Area
(Weighted Avg.)
0.97 0.74 0.96
TABLE V: Comparison of Statistical Metrics for SEED 3
Evaluation Metrics
Machine Learning Algorithms
Bagging Logistic
Random
Forest
Correctly Classified Instances 89.12% 83.24% 87.65%
Incorrectly Classified Instances 10.88% 16.77% 12.35%
Kappa statistic 0.79 0.69 0.76
KB Information Score 305.73 bits 279.89 bits 320.91 bits
Class complexity — order 0 454.75 bits 454.75 bits 454.745 bits
Class complexity — scheme 176.04 bits 8551.65 bits 146.77 bits
Mean absolute error 0.14 0.15 0.12
Root mean squared error 0.25 0.32 0.23
Relative absolute error 39.42% 41.77% 34.05%
Root relative squared error 58.07% 75.61% 54.37%
Coverage of cases
(0.95 level)
98.82% 89.12% 99.41%
Mean rel. region size
(0.95 level)
64.80% 49.12% 59.71%
Specificity/TN Rate
(Weighted Avg.)
0.92 0.87 0.90
Precision
(Weighted Avg.)
0.89 0.83 0.88
Recall
(Weighted Avg.)
0.89 0.83 0.88
F-Measure
(Weighted Avg.)
0.89 0.83 0.87
MCC
(Weighted Avg.)
0.79 0.69 0.76
ROC Area
(Weighted Avg.)
0.95 0.85 0.97
PRC Area
(Weighted Avg.)
0.94 0.79 0.96
Table IV presents the same variables for seed 2. CCI for
BAG, LR RF are 87.6471%, 80.2941% 8.8235% respec-
tively and 0.7604, 0.6417, 0.7832. The values of Specificity
in this case for BAG, LR Rf are respectively 0.868, 0.809
0.903 and 0.882, 0.804 0.896 are for PRE. In addition,
REC and F-Measure for BAG are 0.876 0.872, for LR both
are same which is 0.803 and for RF 0.888 0.885. MCC for
TABLE VI: Comparison with Other Systems
Reference
Number
Sample
Size
No. of
Features
Algorithms Accuracy
Perspective
of the paper
[5] 768 8
K-means
with j48
Decision
Tree (DT)
90.04% Classification
[7] 4322 2
Ensemble
Boosting
with
Perceptron
Algorithm
(EPA)
75%
Ensemble
Learning
[8] 768 9
ANN 80.86%
Classification
KNN 77.24%
[9] 768 8
LR 77.47%
Classification
Deep Neural
Network
(DNN)
77.86%
Support
Vector
Machine
(SVM)
77.60%
DT 76.30%
Nave Bayes
(NB)
75.78%
[11] 3075 14
Bagging
with
DT, RF
LR
89.17% Classification
[12] 768 9
NB, SMO,
MLP,
REP Tree
J48
76.80% Classification
Our
Proposed
System
340 27
Bagging 89.12%
Classification
Logistic
Regression
83.24%
Random
Forest
90.29%
BAG is 0.762, for LR 0.633 and for RF it is 0.787.
The results of performance measurable factors for seed 3
have been shown in Table V. Specificity for BAG is 0.919
and 0.873, 0.904 for LR RF. PRE are 0.893, 0.832, 0.882
and REC 0.891, 0.832, 0.876 for BAG, LR RF respectively.
ROC and PRC Area are 0.950, 0.851, 0.974 and 0.940, 0.791,
0.966 respectively for BAG, LR RF.
BAG and LF provided the best accuracy in seed 3 that
means after they were shuffled for 3 times. On the other hand,
the RF algorithm impressively given the best accuracy in its
first seed. So, comparatively, the RF is the best algorithm
among them.
Table VI illustrates comparisons between various previous
systems with our proposed system. The models have been
compared based on sample size, number of features, algorithms
and accuracy.
Among our proposed systems, the system with Random
Forest algorithm has given the highest accuracy that is 90.29%,
which is the best accuracy by compared with the algorithms
used in the previous systems.
V. CONCLUSION
Despite of having major limitations, the study has finished
successfully with expected outcomes. Collection of real time
data was one of the main problems we have faced at the
initial states and after managed it another bound was to fill
up the missing data since there was several missing data in
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7. the dataset. But using MLT, we have recovered the issues and
performed the analysis to achieve our goal. Among the three
algorithms, RF gives the best performance than BAG LR and
BAG performed better than LR. In future, we will conduct this
study with more algorithms like ANN more specifically with
Neuro Fuzzy Inference System, CNN (Convolution Neural
Network) and advanced Ensemble Learning algorithms. An
expert system can be developed with our analysis to predict
diabetes more efficiently and effectively.
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