ABSTRACT
In this paper, we have researched the prediction of heart illness and liver infection through the process of machine learning (ML) in the point of data analytics. We already know that the medical services are giving enormous information on this disease data. So we can use the ML algorithms, which can be useful in diagnosis.
Prediction of diseases like heart and liver diseases is becoming a very recent field as the data is becoming available. There are such countless strategies to anticipate these illnesses; we have utilized machine learning (ML) models, for example, logistic regression and random forest classifier to predict these diseases.
Prior to finishing these two models, we tried the dataset with various machine learning models like logistic regression, KNN, decision tree classifier, SVM, and random forest classifier in order to know about the accuracy of each of them on the given datasets.
In comparing the accuracy results, we found that logistic regression is performing much better results for heart disease classification and random forest classifier is performing much better results for liver disease prediction.
