Development of ensemble model for heart disease diagnosis (original) (raw)
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Prediction System for Heart Disease Based on Ensemble Classifiers
2020
The heart is an essential organ in the human body. On the off chance that this organ gets influenced, at that point, it equally influences the other fundamental pieces of the body. Heart diseases are the front runner in terms of death worldwide, making the need for an effective prediction system a source of high demand in treating affected patients. This study aims to analyze prediction systems, thereby designing an automated medical diagnosis system that takes advantage of the collected database. For this study, ensemble classifiers were implemented for classification of data of a medical database with discretization used during the preprocessing phase. The data employed in this research was obtained from the University of California (UCI) machine learning repository. The dataset utilized was the Statlog heart disease. Performance measures, such as accuracy, sensitivity, and specificity, were used to evaluate the proposed methods’ performance. The proposed method achieved an accura...
An ensemble based decision support framework for intelligent heart disease diagnosis
International Conference on Information Society (i-Society 2014), 2014
Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different data mining techniques for heart disease diagnosis. Using a single data mining technique shows an acceptable level of accuracy for disease diagnosis. Recently, more research is carried out towards hybrid models which show tremendous improvement in heart disease diagnosis accuracy. The objective of the proposed research is to predict the heart disease in a patient more accurately. The proposed framework uses majority vote based novel classifier ensemble to combine different data mining classifiers. UCI heart disease dataset is used for results and evaluation. Analysis of the results shows that the sensitivity, specificity and accuracy of the ensemble framework are higher as compared to the individual techniques. We obtained 82% accuracy, 74% sensitivity and 93% specificity for heart disease dataset.
Enhancing Heart Disease Prediction Using Ensemble Techniques
SLU Journal of Science and Technology
Background: Cardiovascular diseases are recognized generally to be among the number one illnesscausing death across the globe. Predicting heart disease using a computer-aided technique makes it easier for medical practitioners to diagnose and thereby savinglives andreducingcosts. Feature selection has become an essential component for developing Machinelearning models. It chooses the most relevant features from the available dataset,thereby shortening the training period, making the model easier to train, improving generalization and decreasing overfitting without necessarily compromising the system’s accuracy. Aim:The purpose of this work is to design and build an optimal model forthe prediction of heart diseases,especially at an early stage by considering certain features that are most relevant forthe prediction without compromising the system’s accuracy. Method: The Cleveland UCI dataset with 303 instances wereused in trainingthe model and the findings showthat selectKBest is an ...
Using Ensembles and Machine Learning Techniques to Classify Heart Diagnosis
International Journal of Contemporary Research in Multidisciplinary, 2023
The term heart disease refers to a variety of diseases that affect the function of the heart. These diseases may affect the heart muscle, its valves, and the membrane surrounding it, or the primary arteries and veins to and from the heart. Heart diseases begin with acute pain attacks because of A blockage in one of the veins that delivers blood and oxygen to the heart, and thus the rate of oxygen reaching the heart decreases, or it may stop completely, causing heart attacks, angina pectoris, and other chronic diseases, which might represent a danger to the patient's life. According to the Centers for Disease Control and Prevention (CDC), heart disease is the leading cause of death in the United States, accounting for a quarter of all deaths. Due to the seriousness of this disease, many researchers have been motivated to search for methods and algorithms that reduce the risk of this research, and there are previous works in this way. preprocessing such as replace missing value with mean and detect outliers with KNN K-near nieghbar, then this work was evaluated using the following criteria: accuracy, f-measure , Recall, precision Among the results, the highest value was obtained in this research, reaching 100 with the bagging algorithm.
HEART DISEASE PREDICTION USING ENSEMBLE APPROACH
Journal ijetrm, 2024
A heart attack, also known as a myocardial infarction, occurs when the flow of blood to a part of the heart muscle is suddenly blocked. This blockage prevents the heart from receiving enough oxygen. If blood flow is not promptly restored, the heart muscle begins to die. Machine learning algorithms are a powerful tool for predicting heart disease, allowing for more accurate results than traditional methods. Different models are compared and evaluated to identify the most accurate algorithm. Early detection is key to managing heart disease, and machine learning can help to identify at-risk individuals. Prevention of heart disease is invaluable for saving lives and reducing medical costs. Machine learning algorithms can provide the most accurate predictions possible, offering the best chance of preventing serious health issue
Ensemble technique to predict heart disease using machine learning classifiers
Network Biology, 2023
The exact forecast of heart disease is necessary to proficiently treat cardiovascular patients before a heart failure happens. Assuming we talk about AI techniques can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features (5, 7, 9, and 11) from coronary illness dataset to create important medical services information. The stacking model is prepared for coronary illness forecast. A comparison model is made between datasets with prominent features (5, 7, 9, and 11) as well as all features. The proposed framework is assessed with coronary illness information and contrasted and customary classifiers in view of feature elimination include determination strategies. The proposed framework acquires test accuracy of 98.78%, which is most noteworthy in marking model with 11-featuers and higher than existing frameworks. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies.
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning
Accurate diagnosis is required before performing proper treatments for coronary heart disease. Machine learning based approaches have been proposed by many researchers to improve the accuracy of coronary heart disease diagnosis. Ensemble learning and cascade generalization are among the methods which can be used to improve the generalization ability of learning algorithm. The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization. Cascade generalization method with loose coupling strategy is proposed in this study. C4.5 and RIPPER algorithm were used as meta-level algorithm and Naive Bayes was used as baselevel algorithm. Bagging and Random Subspace were evaluated for constructing the ensemble. The hybrid cascade ensemble methods are compared with the learning algorithms in non-ensemble mode and non-cascade mode. The methods are also compared with Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble method demonstrated the best result for the given heart disease diagnosis case. Accuracy and diversity evaluation was performed to analyze the impact of the cascade strategy. Based on the result, the accuracy of the classifiers in the ensemble is increased but the diversity is decreased.
Biomedical Materials & Devices
The exact forecast of heart disease is necessary to proficiently treating cardiovascular patients before a heart failure happens. Assuming we talk about artificial intelligence (AI) techniques, can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features from coronary illness dataset to create important medical services information. Using machine learning algorithms like Decision tree (DT), Random forest (RF), Multilayer perceptron (MLP), Support vector machine (SVM), Extra tree (ET), Gradient boosting (GBC), Linear regression (LR), K-nearest neighbor (KNN), and stacking, a comparison model is created between dataset that include both all features and a significant number of features. With stacking, the proposed framework achieves test accuracy of 98.78 percent, which is higher than the existing frameworks and most notable in the marking model with 11 features. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies.
An Ensemble Classifier for the Prediction of Heart Disease
Heart disease has become a silent killer among people of all ages. The major risk factors of heart disease include smoking, blood pressure, cholesterol, diabetes etc. Early diagnosis and treatment can reduce morbidity rate to an extent by identifying patients at higher risk of having a heart disease and providing them right care at right time. However provisioning of quality services at reasonable costs is a major concern of every healthcares. Poor clinical decisions can pose adverse effects on human health. This paper introduces a method based on data mining according to the information of patients' medical records to predict heart disease. An ensemble classifier approach is being used, that is the combination of three classifiers (KNN, Decision Tree, NaiveBayes) composing an ensemble, so that the overall model can be used to give predictions with greater accuracy than that of individual classifiers.
Hybrid Classification Using Ensemble Model to Predict Cardiovascular Diseases
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Machine Learning is a widely used tool in the healthcare industry. Machine Learning algorithms help to predict and detect the presence of cardiovascular diseases. Such information, if predicted ahead of time, can provide important knowledge to doctors who can then diagnose and deal per patient basis. We work on predicting possible heart diseases in people using Machine Learning algorithms. In this project we perform the comparative analysis of classifiers like Naïve Bayes, SVM, Logistic Regression, Decision trees and Random Forest and we propose an ensemble classifier which perform hybrid classification by taking classifiers(strong and weak) since it can have multiple number of samples for training and validating the data so we perform the analysis of existing classifier and proposed classifier like Ada-boost and XG-boost combined with logistic regression and which can give the better accuracy and predictive analysis.