AI Assisted Heart Disease Prediction and Classification and Segmentation based on PIMA and UCI Machine Learning Datasets

Abstract

The article is devoted to the development of an artificial intelligence-driven system of heart disease diagnosis. Based on the machine learning algorithms. We demonstrate how machine learning might be useful to predict the possibility of an individual coming down with heart disease. An AI-based model is developed using the Python language is proposed in this paper to research the healthcare sector is more credible and assists in monitoring and setting up various health monitoring application levels. We present processing data, which involves the manipulation of categorical data as well as the transformation of categorical columns. We outline major stages of application development, which are: collection of databases, logistic regression, and analysis of the attributes of the dataset. The random forest classifier algorithm is created to detect heart diseases in a more accurate manner. This application requires data analysis, which is quite important to the organization. It's about an 83% precision level against training sets. Thereafter, we address the random forest classifier algorithm. such as the experiments and the findings, which give improved accuracies on research diagnoses. We conclude the purpose, the limitations and the research contributions of the paper

Keywords:  Artificial Intelligence, Machine Learning, Logistic Regression, Random Forest, Neural Networks, SVM, Data Preprocessing, Predictive Modeling