This document discusses the implementation of a machine learning-based web application to predict and manage diabetes in women. Several machine learning algorithms were tested on a diabetes dataset, including logistic regression, decision trees, random forest, SVM, KNN, AdaBoost and gradient boosting. The top 5 algorithms achieved accuracies between 67-80%. These 5 algorithms were then combined using an ensemble voting classifier, which achieved an accuracy of 82% for diabetes prediction. The proposed web application uses the machine learning model for early diabetes detection and also provides dietary and exercise recommendations for pre-treatment management.
ML In Predicting Diabetes In The Early StageIRJET Journal
This document discusses machine learning methods for predicting diabetes in the early stages. It begins with an introduction to diabetes and the need for early detection. The document then describes the dataset and various machine learning algorithms used, including XGBoost, K-nearest neighbors, decision trees, random forest, SVM, Naive Bayes and neural networks. The methods section provides details on the dataset, data preprocessing including cleaning, compression and transformation. It also provides diagrams of the software architecture and algorithms. Accuracy metrics like precision, recall and F1 score are discussed to evaluate model performance. The goal is to develop a system that can more accurately predict early diabetes by combining results from multiple machine learning methods.
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
Early Stage Diabetic Disease Prediction and Risk Minimization using Machine L...IRJET Journal
This document reviews machine learning techniques for early prediction and risk minimization of diabetic disease. It discusses how various machine learning algorithms like decision trees, KNN, random forests, and SVM have been applied to diabetes prediction datasets. Accuracy rates of 83.11% to 88.42% were achieved for different algorithms. Feature selection techniques like Pearson correlation were also able to improve some algorithm accuracies further. The document proposes using machine learning systems to better diagnose and care for diabetic patients early on.
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.
Forecasting Diabetes Mellitus at an Initial Stage using Machine Learning MethodsIRJET Journal
This document presents a study that uses machine learning methods to develop a model for predicting diabetes at an early stage. The researchers used a dataset of 520 patient records containing 17 attributes. They applied preprocessing techniques like encoding categorical variables and splitting the data into training and test sets. Four machine learning algorithms were implemented: Multilayer Perceptron, K-Nearest Neighbor, Gaussian Naive Bayes, and Linear Discriminant Analysis. The Multilayer Perceptron model achieved the highest accuracy of 99% on the test data, making it suitable for predicting diabetes risk or an initial diabetes diagnosis.
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.
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 EFFECTIVE PREDICTION OF CHRONIC KIDENY DISEASE USING DATA MINING CLASSIFIE...IRJET Journal
This document discusses using data mining classifiers and data sampling techniques to effectively predict chronic kidney disease. It analyzes the chronic kidney disease dataset from the UCI machine learning repository, which is imbalanced. It proposes applying various data sampling methods like SMOTE, ADASYN, SMOTE+Tomek Links, and K-means SMOTE to balance the dataset. Then, different data mining algorithms like decision trees, random forests, SVM, and KNN will be used on the sampled datasets to predict chronic kidney disease. The goal is to find the best performing combination of sampling technique and data mining algorithm based on accuracy, precision, sensitivity and specificity metrics.
ML In Predicting Diabetes In The Early StageIRJET Journal
This document discusses machine learning methods for predicting diabetes in the early stages. It begins with an introduction to diabetes and the need for early detection. The document then describes the dataset and various machine learning algorithms used, including XGBoost, K-nearest neighbors, decision trees, random forest, SVM, Naive Bayes and neural networks. The methods section provides details on the dataset, data preprocessing including cleaning, compression and transformation. It also provides diagrams of the software architecture and algorithms. Accuracy metrics like precision, recall and F1 score are discussed to evaluate model performance. The goal is to develop a system that can more accurately predict early diabetes by combining results from multiple machine learning methods.
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
Early Stage Diabetic Disease Prediction and Risk Minimization using Machine L...IRJET Journal
This document reviews machine learning techniques for early prediction and risk minimization of diabetic disease. It discusses how various machine learning algorithms like decision trees, KNN, random forests, and SVM have been applied to diabetes prediction datasets. Accuracy rates of 83.11% to 88.42% were achieved for different algorithms. Feature selection techniques like Pearson correlation were also able to improve some algorithm accuracies further. The document proposes using machine learning systems to better diagnose and care for diabetic patients early on.
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.
Forecasting Diabetes Mellitus at an Initial Stage using Machine Learning MethodsIRJET Journal
This document presents a study that uses machine learning methods to develop a model for predicting diabetes at an early stage. The researchers used a dataset of 520 patient records containing 17 attributes. They applied preprocessing techniques like encoding categorical variables and splitting the data into training and test sets. Four machine learning algorithms were implemented: Multilayer Perceptron, K-Nearest Neighbor, Gaussian Naive Bayes, and Linear Discriminant Analysis. The Multilayer Perceptron model achieved the highest accuracy of 99% on the test data, making it suitable for predicting diabetes risk or an initial diabetes diagnosis.
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.
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 EFFECTIVE PREDICTION OF CHRONIC KIDENY DISEASE USING DATA MINING CLASSIFIE...IRJET Journal
This document discusses using data mining classifiers and data sampling techniques to effectively predict chronic kidney disease. It analyzes the chronic kidney disease dataset from the UCI machine learning repository, which is imbalanced. It proposes applying various data sampling methods like SMOTE, ADASYN, SMOTE+Tomek Links, and K-means SMOTE to balance the dataset. Then, different data mining algorithms like decision trees, random forests, SVM, and KNN will be used on the sampled datasets to predict chronic kidney disease. The goal is to find the best performing combination of sampling technique and data mining algorithm based on accuracy, precision, sensitivity and specificity metrics.
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.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEMEPublication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEME Publication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
Diagnosis of Diabetes Mellitus Using Machine Learning TechniquesIRJET Journal
The document discusses diagnosing diabetes mellitus using machine learning techniques. It analyzes medical records of diabetics using two classification algorithms: Random Forest and Support Vector Machine (SVM). The SVM algorithm is analyzed using different kernel functions and the best one is selected for prediction. Random Forest uses decision trees to make predictions. The purpose is to predict diabetes and compare the accuracy of the two algorithms to find the best for diabetes prediction.
This document discusses using machine learning models to predict diabetes. It begins by introducing machine learning and its applications in healthcare for early disease detection. It then discusses existing disease prediction systems and proposes a new system using supervised learning methods to predict diseases like diabetes based on symptoms. The rest of the document focuses on diabetes, describing the disease and its symptoms. It also discusses different machine learning techniques like supervised, unsupervised, semi-supervised and reinforcement learning that can be used to develop a model for diabetes prediction. Finally, it outlines the key steps to develop a machine learning model, including data collection, preparation, transformation and using the data to train a model.
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
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.
This document discusses using machine learning strategies to predict diabetes more accurately. It analyzes various machine learning algorithms (KNN, logistic regression, decision trees, SVM, random forest, gradient boosting) on a diabetes dataset. The results show that random forest achieved the highest accuracy compared to other methods at predicting diabetes. The proposed methodology uses different machine learning algorithms and ensemble techniques to build predictive models and determine the most accurate one for diabetes prognosis.
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
The document describes an improved model for a clinical decision support system that was developed to address issues with misdiagnosis and inconsistent healthcare records. The system incorporates both knowledge-based and non-knowledge based decision support methods using a hybrid approach. It was trained and validated using prostate cancer and diabetes datasets, achieving classification accuracies of 98% and 94% respectively. The system aims to enhance disease detection and prediction to support better healthcare delivery.
Diabetes prediction using machine learningdataalcott
This document discusses a proposed system to classify and predict diabetes using machine learning and deep learning algorithms. The objectives are to classify the PIMA Indian diabetes dataset and design an interactive application where users can input data to get a prediction. The proposed system uses support vector machine (SVM) for machine learning and neural networks for deep learning. It aims to improve accuracy over existing systems by using deep learning techniques. The methodology involves collecting a dataset, preprocessing, splitting for training and testing, applying algorithms, and evaluating results.
ELECTRONIC HEALTH RECORD USING THE SPARSE BALANCE SUPPORT VECTOR MACHINE FOR ...IRJET Journal
The document presents a machine learning approach called Sparse Balanced Support Vector Machine (SB-SVM) for identifying Type 2 Diabetes (T2D) using electronic health record (EHR) data. SB-SVM aims to address challenges with imbalanced class distributions and high dimensionality in EHR data. It applies lasso regularization to select relevant features and balances classes by modifying the decision boundary. The approach was tested on a novel EHR dataset and showed better performance than other machine learning and deep learning methods, providing an accurate prediction with lower computational cost and improved interpretability through sparse modeling.
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.
This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
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.
IRJET- Predicting Diabetes Disease using Effective Classification TechniquesIRJET Journal
This document discusses predicting diabetes disease using machine learning techniques. It begins with an abstract introducing diabetes mellitus and the importance of early detection. It then discusses the Pima Indian diabetes dataset that is commonly used for research. The document outlines the existing research which focuses mainly on one or two techniques, while the proposed research will take a more comprehensive approach, comparing multiple techniques. It describes evaluating classifiers like deep neural networks and support vector machines on the Pima Indian dataset. The best technique identified achieved 77.86% accuracy. Feature relevance is also analyzed to modify the dataset for future studies. The goal is to automate diabetes identification and help physicians detect the disease earlier.
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
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.
IRJET - Deep Multiple Instance Learning for Automatic Detection of Diabetic R...IRJET Journal
This document describes a proposed method for using deep multiple instance learning to automatically detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that can cause vision loss or blindness. The proposed method treats retinal images as "bags" containing "instances" of image patches. A deep learning model is trained using only image-level labels to both detect diabetic retinopathy images and identify lesions within images. The model first preprocesses images to normalize factors like scale and illumination. It then segments lesions and extracts features before classifying images using convolutional neural networks. The goal is to provide explicit locations of lesions to aid clinicians while leveraging large datasets typically required for deep learning.
Prediction of Diabetes using Probability ApproachIRJET Journal
This document discusses using a Bayesian Network classifier to predict whether individuals have diabetes based on various attributes. It analyzes a Pima Indian Diabetes dataset containing information on individuals with and without diabetes. The study aims to help identify diabetes and improve people's lifestyles by making them aware of the disease and how to treat it. It evaluates the prediction performance of Bayesian algorithms for classifying individuals as diabetic or non-diabetic.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
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.
IRJET- Predicting Diabetes Disease using Effective Classification TechniquesIRJET Journal
This document discusses predicting diabetes disease using machine learning techniques. It begins with an abstract introducing diabetes mellitus and the importance of early detection. It then discusses the Pima Indian diabetes dataset that is commonly used for research. The document outlines the existing research which focuses mainly on one or two techniques, while the proposed research will take a more comprehensive approach, comparing multiple techniques. It describes evaluating classifiers like deep neural networks and support vector machines on the Pima Indian dataset. The best technique identified achieved 77.86% accuracy. Feature relevance is also analyzed to modify the dataset for future studies. The goal is to automate diabetes identification and help physicians detect the disease earlier.
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
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.
IRJET - Deep Multiple Instance Learning for Automatic Detection of Diabetic R...IRJET Journal
This document describes a proposed method for using deep multiple instance learning to automatically detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that can cause vision loss or blindness. The proposed method treats retinal images as "bags" containing "instances" of image patches. A deep learning model is trained using only image-level labels to both detect diabetic retinopathy images and identify lesions within images. The model first preprocesses images to normalize factors like scale and illumination. It then segments lesions and extracts features before classifying images using convolutional neural networks. The goal is to provide explicit locations of lesions to aid clinicians while leveraging large datasets typically required for deep learning.
Prediction of Diabetes using Probability ApproachIRJET Journal
This document discusses using a Bayesian Network classifier to predict whether individuals have diabetes based on various attributes. It analyzes a Pima Indian Diabetes dataset containing information on individuals with and without diabetes. The study aims to help identify diabetes and improve people's lifestyles by making them aware of the disease and how to treat it. It evaluates the prediction performance of Bayesian algorithms for classifying individuals as diabetic or non-diabetic.
Similar to Implementation of a Web Application to Foresee and Pretreat Diabetes Mellitus in Women using Machine Learning (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.