Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 13
• Main Authors: Nadakinamani, Rajkumar Gangappa, Reyana, A., Kautish, Sandeep, Vibith, A. S., Gupta, Yogita, Abdelwahab, Sayed F., Mohamed, Ali Wagdy
• Format: Journal Article
• Language: English
• Published: United States Hindawi 11.01.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Blood pressure; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cholesterol; COVID-19; Data Analysis; Data mining; Datasets; Decision making; Decision trees; Health care industry; Heart rate; Humans; Hypertension; Learning algorithms; Machine Learning; Medical research; Medicine, Experimental; Methods; Mortality; Predictions; Risk analysis; Risk factors; Hungary; India
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/2973324

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.