Precise Prediction of Cardiovascular Diseases Using Machine Learning

Cardiovascular disease is among the conditions that pose the greatest risk to life. Nearly 17 million people die as a result of its high mortality rate worldwide. To treat the illness quickly and reduce mortality, early diagnosis is helpful. The occurrence and absenteeism of the ailment can be hush-hush consuming a variability of ML techniques. The UCI dataset is to categorize heart disease using the techniques of LR, NB,SVM and Convolution Neural Networks(CNN). To progress the model’s recital, the dataset was cleaned, missing value searches were carried out, and feature selection was done through correlation by the goal worth for all of the features. The traits with the highest favourable associations were picked. The dataset is then alienated into train and trial sets, and classification is completed experimenting with a 70:30:80 ratio. The most accurate dividing ratio is 80:20. The best outcome will be recorded and used in the suggested model, which will compare Logistic regression, Naive Bayes, and Support vector machines with and without feature selection. Among all the models CNN shows the best result.