scholarly journals Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ebenezer Owusu ◽  
Prince Boakye-Sekyerehene ◽  
Justice Kwame Appati ◽  
Julius Yaw Ludu
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Missing Values ◽  
Disease Risk ◽  
Heart Diseases ◽  
Cleveland Clinic ◽  
Diagnostic Tools ◽  
Support Vector ◽  
Computer Aided ◽  
Heart Disease Risk

Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

scholarly journals Heart Disease Risk Predictor using Support Vector Machine

2019 ◽  
Vol 8 (2) ◽  
pp. 4629-4636
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Medical History ◽  
Disease Risk ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Mining Technique ◽  
Disease Risk Factor ◽  
Risk Predictor ◽  
Heart Disease Risk

Nearly 17.5 million deaths occur due to cardiovascular diseases throughout the world. If we could create such a mechanism or system that could tell people about their heart condition based on their medical history and warn them of any risk than it could be of huge help. In our work, we will use machine learning algorithms to forecast the heart disease risk factor for a person depending upon some attributes in their medical history. The data mining technique Naive Bayes, Decision tree, Support Vector Machine, and Logistic Regression is analyzed on the Heart disease database. The accuracy of different algorithms is measured and then the algorithms are compared.

HIV: cardiovascular disorders

2019 ◽  
pp. 589-594
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Disease Risk ◽  
Heart Diseases ◽  
Hiv Treatment ◽  
Lifestyle Changes ◽  
Risk Profiling ◽  
Drug Treatments ◽  
Heart Disease Risk

This chapter discusses key cardiovascular conditions that effect people who live with HIV. HIV can lead to direct effect on the heart and the drug treatments may modify risk factors for heart disease. The chapter reviews the epidemiology of heart diseases in people who live with HIV . Specific disease processes are discussed, including cardiomyopathy, pericardial effusion, myocarditis, and endocarditis. Effect of HIV treatment on cardiovascular risk is discussed. Cardiovascular disease in people who live with HIV is reviewed with a focus on lifestyle changes, and effect of drugs on the heart and risk factors for heart disease. Risk profiling of cardiovascular disease is outlined with some discussion of treatment.

scholarly journals Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 604-609
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Heart Diseases ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Set ◽  
Raw Data ◽  
Reduced Data

In today’s modern world, the human beings are affected with heart disease irrespective of the age. With the advancement of technological growth, predicting the availability of Heart diseases still remains a challenging issue. The difficulty of predicting the heart disease prevails due to the lack of availability of the symptoms. According to World Health Organization, 33% of population died due to heart diseases. For this, the diagnosis of heart diseases is made by complex combination of clinical data. With this overview, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for predicting the level of heart disease. The prediction of heart disease classes are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, the raw data set is fitted to classifiers like logistic regression, KNN classifier, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, Random Forest and Decision Tree classifiers. Third, the raw data set is subjected to dimensionality reduction using Principal Component Analysis to project the dataset with important components. The dimensionality PCA reduced data set is fitted to the above-mentioned classifiers. Fourth, the performance comparison of raw data set and PCA reduced data set is done by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Random forest is found to be effective with the accuracy of 89% without applying PCA, 85% with five component PCA and 86% with seven component PCA.

Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques

2021 ◽  
pp. 212-221
Author(s):  
Sarangam Kodati ◽  
Jeeva Selvaraj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Heart Diseases ◽  
Support Vector ◽  
Primary Reason ◽  
The World ◽  
Mining Methods ◽  
Using Data

Data mining is the most famous knowledge extraction approach for knowledge discovery from data (KDD). Machine learning is used to enable a program to analyze data, recognize correlations, and make usage on insights to solve issues and/or enrich data and because of prediction. The chapter highlights the need for more research within the usage of robust data mining methods in imitation of help healthcare specialists between the diagnosis regarding heart diseases and other debilitating disease conditions. Heart disease is the primary reason of death of people in the world. Nearly 47% of death is caused by heart disease. The authors use algorithms including random forest, naïve Bayes, support vector machine to analyze heart disease. Accuracy on the prediction stage is high when using a greater number of attributes. The goal is to function predictive evaluation using data mining, using data mining to analyze heart disease, and show which methods are effective and efficient.

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