Authors:
Soumyendu Bhattacharjee,Susmita Das,Sangita Roy,Arpita Santra,Anasuya Sarkar,Moumita Pal,Biswarup Neogi,DOI NO:
https://doi.org/10.26782/jmcms.2025.06.00007Keywords:
Predictive Model,Random Forest Classifier,KNN,Logistic Regression,Abstract
Because it regularly results in more spending than any other explanation, coronary disease is the leading source of fear and mortality on a global scale. The WHO estimates that 17.9 million people died annually from heart disease in 2016, which accounted for 31% of all deaths. More than 75% of these fatalities occurred in developing and middle-income nations. We create a coronary disease prediction model based on the patient's clinical history to assess whether or not the patient is most likely to develop a coronary illness. We used several artificial intelligence (AI) techniques, such as the critical backslide and KNN, to predict and group patients with cardiovascular sickness. The given coronary illness hypothesis system utilizes clinical reasoning and reduces the cost. We categorize a patient based on 14 medical characteristics or features to determine whether they are likely to develop a heart condition to anticipate this. Three algorithms are used to train these medical features: Random Forest Classifier, KNN, and Logistic Regression.Refference:
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