This document describes a study that uses a genetically optimized neural network to classify heart disease based on patient risk factors. The study collects data on 12 risk factors from 50 patients and encodes the values for use as input to a neural network. The neural network is initially trained using backpropagation, then genetic algorithms are used to optimize the network weights and biases to improve accuracy. Confusion matrices are plotted to evaluate the accuracy of the optimized neural network at classifying patients as having heart disease or not. The approach achieves a classification accuracy of 90% on the test data.
IRJET- Role of Different Data Mining Techniques for Predicting Heart DiseaseIRJET Journal
This document discusses the use of data mining techniques to predict heart disease. It provides an overview of different techniques like association, classification, clustering, and prediction that have been used by researchers. These techniques include decision trees, neural networks, naive Bayes, k-means clustering, and support vector machines. The document then reviews other related work where researchers have applied these techniques to cardiovascular disease datasets, reducing parameters to improve accuracy and comparing methods like naive Bayes, decision trees and neural networks. The goal is to develop early prediction systems for heart disease using data mining.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
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.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
A Comparative Analysis of Heart Disease Prediction System Using Machine Learn...IRJET Journal
This document presents a literature review and comparative analysis of existing machine learning techniques used for heart disease prediction. It discusses several studies that have applied techniques like decision trees, naive Bayes, KNN, random forest, SVM and neural networks to heart disease datasets. The highest prediction accuracies reported range from 85-100%, with random forest and KNN performing best in some studies. The document aims to help researchers develop better heart disease prediction systems by understanding existing methodologies and identifying areas for improvement.
This document describes a heart disease prediction system that uses machine learning algorithms to analyze patient data and predict the presence and severity of heart disease. The system uses four algorithms - random forest, naive bayes, decision tree, and linear regression - to build predictive models using a dataset of 801 patients with 12 medical attributes. The models are evaluated on their accuracy in both detecting heart disease and classifying its severity from 0 to 4. Random forest achieved the highest accuracy of 95.09% while naive bayes had the lowest at 60.38%. The system provides a way to more accurately diagnose heart disease early using data mining of existing patient records.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
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.
IRJET- Role of Different Data Mining Techniques for Predicting Heart DiseaseIRJET Journal
This document discusses the use of data mining techniques to predict heart disease. It provides an overview of different techniques like association, classification, clustering, and prediction that have been used by researchers. These techniques include decision trees, neural networks, naive Bayes, k-means clustering, and support vector machines. The document then reviews other related work where researchers have applied these techniques to cardiovascular disease datasets, reducing parameters to improve accuracy and comparing methods like naive Bayes, decision trees and neural networks. The goal is to develop early prediction systems for heart disease using data mining.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
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.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
A Comparative Analysis of Heart Disease Prediction System Using Machine Learn...IRJET Journal
This document presents a literature review and comparative analysis of existing machine learning techniques used for heart disease prediction. It discusses several studies that have applied techniques like decision trees, naive Bayes, KNN, random forest, SVM and neural networks to heart disease datasets. The highest prediction accuracies reported range from 85-100%, with random forest and KNN performing best in some studies. The document aims to help researchers develop better heart disease prediction systems by understanding existing methodologies and identifying areas for improvement.
This document describes a heart disease prediction system that uses machine learning algorithms to analyze patient data and predict the presence and severity of heart disease. The system uses four algorithms - random forest, naive bayes, decision tree, and linear regression - to build predictive models using a dataset of 801 patients with 12 medical attributes. The models are evaluated on their accuracy in both detecting heart disease and classifying its severity from 0 to 4. Random forest achieved the highest accuracy of 95.09% while naive bayes had the lowest at 60.38%. The system provides a way to more accurately diagnose heart disease early using data mining of existing patient records.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
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.
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.
Predicting Heart Disease Using Machine Learning Algorithms.IRJET Journal
This document summarizes a research paper that predicts heart disease using machine learning algorithms. It compares the performance of three algorithms - logistic regression, decision trees, and random forests - on a heart disease dataset. Logistic regression achieved the highest accuracy at 92%, outperforming decision trees and random forests. The paper outlines developing a heart disease prediction web application using logistic regression that allows users to input their medical details and get a prediction of their heart disease risk level.
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.
A COMPREHENSIVE SURVEY ON CARDIAC ARREST RISK LEVEL PREDICTION SYSTEMIRJET Journal
This document summarizes research on predicting cardiac arrest risk levels using machine learning techniques. It discusses how techniques like naive Bayes, support vector machine, KNN, logistic regression, decision trees, and random forests can be used to classify patient risk levels based on medical data. Accuracy rates from prior studies using these methods on cardiac datasets ranged from 60% to over 99%, depending on the techniques and attributes used. The document also outlines some challenges in cardiac risk prediction, such as choosing the appropriate dataset, attributes, algorithms and evaluating model performance.
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET Journal
The document compares four algorithms - K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest - for cardiovascular disease detection using data mining techniques. It summarizes previous studies that have used these algorithms on cardiovascular disease data and evaluated their performance. The document concludes that K-Nearest Neighbors, Support Vector Machine, Decision Tree, or Random Forest algorithms could be used for cardiovascular disease detection, and that the best algorithm depends on the specific dataset and type of disease being diagnosed.
IRJET- Prediction and Analysis of Heart Disease using SVM AlgorithmIRJET Journal
This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
Heart disease classification using Random ForestIRJET Journal
This document presents research on using the random forest machine learning algorithm to classify and predict heart disease.
The researchers used a dataset of 303 patient records combining data from 4 databases to train and test their random forest model. They achieved a classification accuracy of 86.9% and diagnosis rate of 93.3% when predicting heart disease risk.
Future work could include improving data quality, collecting a larger and more diverse dataset, further optimizing model hyperparameters, and developing classifiers to predict specific heart conditions rather than a general heart disease risk prediction. The goal of this research is to develop an accurate and efficient heart disease prediction tool to help doctors diagnose diseases early and improve patient outcomes.
Prediction of Heart Disease Using Data Mining Techniques- A ReviewIRJET Journal
This document reviews the use of data mining techniques to predict heart disease. It discusses how medical data sets contain a large amount of patient diagnosis, medication, and detail data that can be mined using techniques like classification algorithms to extract useful patterns and predict trends. Specifically, it explores using classification algorithms like decision trees, Naive Bayes, and neural networks on a data set of 603 records with 76 attributes related to heart health to predict heart disease.
This document discusses using machine learning techniques to predict heart disease. It proposes a new hybrid method combining machine learning techniques to improve prediction accuracy. Specifically, it introduces a prediction model using different feature combinations and classification techniques, including a hybrid random forest with linear model (HRFLM) that achieves 88.7% accuracy. Previous studies predicting heart disease using machine learning are also discussed, but the authors aim to develop a unique method focusing on feature selection to further improve prediction accuracy of heart disease.
Heart Disease Prediction Using Data MiningIRJET Journal
This document discusses using data mining techniques like Naive Bayes and Weighted Associative Classifier (WAC) to predict heart disease. It analyzes a dataset containing factors like age, sex, medical history, and test results. Naive Bayes and WAC are used to generate rules for predicting whether a patient has heart disease risk. The system was able to indicate heart disease risk levels based on the patient's data. The document concludes the approach was effective for heart disease prediction and automation could further improve clinical decision making.
This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
PREDICTION OF HEART DISEASE USING LOGISTIC REGRESSIONIRJET Journal
This document discusses predicting heart disease using logistic regression. It begins with an introduction to heart disease and risk factors. It then discusses previous work using machine learning to predict diseases. The proposed system uses a logistic regression model to predict heart disease risk using 14 attributes from a dataset. It discusses the logistic regression technique and provides a workflow diagram. The results show the logistic regression model achieving 81% accuracy. It concludes that logistic regression is effective for predicting heart disease risk from simple clinical predictors.
Heart Disease Prediction Using Machine Learning TechniquesIRJET Journal
This document describes a study that used five machine learning algorithms to predict heart disease: Random Forest classification, Support Vector Machine, AdaBoost Classifier, Logistic Regression, and Decision Tree Classifier. The algorithms were tested on a dataset of 270 patients described by 14 attributes. Random Forest classification achieved the highest test accuracy of 85.22%, compared to accuracies ranging from 67.43% to 81.23% for the other algorithms. Therefore, the study concludes that Random Forest classification is the best performing algorithm for predicting heart disease based on this dataset and analysis.
Multiple Disease Prediction System: A ReviewIRJET Journal
This document discusses a study analyzing the use of machine learning techniques to predict multiple diseases based on user-inputted symptoms in a multi-disease prediction system. The system employs predictive modelling and examines symptoms to determine potential illnesses and their likelihood. The study focuses on predicting common diseases like diabetes, heart disease, breast cancer, hepatitis, and kidney disease. It evaluates various machine learning algorithms and their ability to accurately predict these diseases from pre-processed healthcare data.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
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.
Risk Of Heart Disease Prediction Using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict the risk of heart disease. It analyzes a dataset containing characteristics of 270 patients using algorithms like logistic regression, naive Bayes, support vector machine, k-nearest neighbors, decision tree, random forest, XGBoost and artificial neural network. The random forest algorithm achieved the highest prediction accuracy of 95%. The model takes patient attributes as input and outputs a prediction of 0 or 1 indicating the presence or absence of heart disease risk. It aims to help detect risk early to reduce death rates from heart disease, which is a leading cause of death worldwide.
1. The document describes a multiple disease prediction system that uses machine learning to predict three diseases: heart disease, liver disease, and diabetes.
2. It aims to build a single system that can predict multiple diseases, unlike existing systems that typically only predict one disease. This would allow users to predict different diseases without needing multiple different tools.
3. The system is designed to take user inputs related to symptoms and features of the selected disease and use machine learning algorithms like KNN, random forest and XGBoost trained on disease datasets to predict the likelihood of the disease. The models would be integrated into a web interface using Django for users to get predictions.
EVALUATING THE ACCURACY OF CLASSIFICATION ALGORITHMS FOR DETECTING HEART DISE...mlaij
The healthcare industry generates enormous amounts of complex clinical data that make the prediction of
disease detection a complicated process. In medical informatics, making effective and efficient decisions is
very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and
interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is
considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to
a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart
diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely,
J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the
impact of the feature selection method. A comparative and analysis study was performed to determine the
best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The
performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity
and specificity. The importance of using classification techniques for heart disease diagnosis has been
highlighted. We also reduced the number of attributes in the dataset, which showed a significant
improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart
disease was Random Forest with an accuracy of 99.24%.
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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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
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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.
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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.
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This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
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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.
Risk Of Heart Disease Prediction Using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict the risk of heart disease. It analyzes a dataset containing characteristics of 270 patients using algorithms like logistic regression, naive Bayes, support vector machine, k-nearest neighbors, decision tree, random forest, XGBoost and artificial neural network. The random forest algorithm achieved the highest prediction accuracy of 95%. The model takes patient attributes as input and outputs a prediction of 0 or 1 indicating the presence or absence of heart disease risk. It aims to help detect risk early to reduce death rates from heart disease, which is a leading cause of death worldwide.
1. The document describes a multiple disease prediction system that uses machine learning to predict three diseases: heart disease, liver disease, and diabetes.
2. It aims to build a single system that can predict multiple diseases, unlike existing systems that typically only predict one disease. This would allow users to predict different diseases without needing multiple different tools.
3. The system is designed to take user inputs related to symptoms and features of the selected disease and use machine learning algorithms like KNN, random forest and XGBoost trained on disease datasets to predict the likelihood of the disease. The models would be integrated into a web interface using Django for users to get predictions.
EVALUATING THE ACCURACY OF CLASSIFICATION ALGORITHMS FOR DETECTING HEART DISE...mlaij
The healthcare industry generates enormous amounts of complex clinical data that make the prediction of
disease detection a complicated process. In medical informatics, making effective and efficient decisions is
very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and
interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is
considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to
a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart
diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely,
J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the
impact of the feature selection method. A comparative and analysis study was performed to determine the
best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The
performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity
and specificity. The importance of using classification techniques for heart disease diagnosis has been
highlighted. We also reduced the number of attributes in the dataset, which showed a significant
improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart
disease was Random Forest with an accuracy of 99.24%.
Similar to Genetically Optimized Neural Network for Heart Disease Classification (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.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
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.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
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.