Automated Detection of Coronary Artery Disease using Machine Learning Algorithm

Abstract

Data mining, an excellent development technology for discovering and gathering essential knowledge from vast data collection that can help analyze and draw up trends for decision-making in the industry. Talking about the medical sphere, data mining can be used to uncover and withdraw useful data and trends that can be helpful in clinical diagnostic results. The research focuses on the diagnosis of heart disease, taking past evidence and information into account. To achieve this SHDP, non-linear SVC with RBF kernel algorithms is designed to perfect this SHDP (Smart Heart Disease Prediction). The final is a useful algorithm to look for the right combination of hyper parameters to increase the precision of the algorithm (C, α). The requisite data was arranged in a structured way. The following features are derived from medical profiles for the estimation of the risks of heart failure in a patient: BP, age, sex, cholesterol, blood sugar, etc. The collected characteristics serve as an input to the Navies Bayesian heart disease prediction classification. The data collection used is divided into two parts, 80% of the data are used for preparation, and 20% are used for research. The method suggested includes data collection, user authentication, and log in (based on application), classification through Navies Bayesian, prediction, and safe data transmission via the AES application (Advanced Encryption Standard). An average accuracy, specificity, sensitivity, precision, 93.53% f-score, 89.22%, 91.24% and 86.98%, respectively. This method is also possible in clinical settings to help clinicians predict cardiac arrest.

Predicting Coronary Artery Disease Using Machine Learning

“…In the study, Sinha and Sharma (2021) employed data mining techniques to develop a smart heart disease prediction (SHDP) algorithm for diagnosing heart disease, the algorithm utilized non-linear support vector classification (SVC) with a radial basis function (RBF) kernel. The dataset was divided into 80% for training and 20% for testing.…”

Development of advanced artificial intelligence techniques for the detection of myocardial infarction ECG signals in clinical settings

“…The authors reported a high classification accuracy of about 95%. In another study, Sinha et al [34] developed a nonlinear support vector classifier with a radial basis function kernel for classifying coronary artery disease using a range of features such as age, gender, ECG outcome, and others. They reported that the classifier yielded an accuracy of 89%.…”