Abstract: Heart disease is a common problem that can be very serious in the elderly and also in individuals who do not have a healthy lifestyle. In addition to maintaining a decent eating habit, it can prevent it to some extent with periodic check-up and diagnosis. Hospitals produce a large amount of patient data, such as heart pain results, chest pain results, personal health records (PHRs), etc. Based on the symptoms, which are explicitly the attributes needed for prediction, the decision tree classifier is implemented. Using the decision tree algorithm, we will be able to classify certain attributes which are the best ones that will lead us to a better prediction of the datasets. The data that is generated from the hospitals are not used effectively. Some of these tools are used to extract data from the heart disease detection database, and other functions are not accepted. Various optimization algorithms such as (Fuzzy Logic, Random Forests, and Q-Learning) and health care data are used in this report to identify patients whether or not they have heart diseases according to the details in the record. The system is able to predict the heart disease as well as COVID-19 disease possibility in a detailed report. Try to use the data as a model that tells the patient whether or not they have heart disease.

Keywords: Heart disease prediction, machine learning, supervised learning, classification, cardiac disease, prediction system

PDF | DOI: 10.17148/IJARCCE.2021.105115

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