Heart Disease Prediction Using Logistic Regression
Main Article Content
Myocardial Infarction and Brain attacks are responsible for the fatalities of individuals from cardiovascular diseases (CVDs), and especially the deaths occur before age 70. 17.9 million people are thought to pass away from CVDs annually. Accurate monitoring for each patient individually is not always possible, and clinicians cannot consult with patients every 24 hours due to the additional time and knowledge required. Using the patient's various cardiac characteristics and the machine learning approach of logistic regression on a publicly accessible dataset from Kaggle, we developed and examined models for predicting heart disease in this research. The main objective is to ascertain of acquiring coronary heart disease (CHD) upto 10 years of health risk. More than 4,000 records, 15 attributes, and patient data are included in the collection. To forecast outcomes, it makes predictions about a dependent variable based on one or more sets of independent variables. Both binary classification and multi-class classification can use it. This study aims to establish the most significant heart disease risk factors and estimate the overall risk using logistic regression.
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