Health Analytics for Prediction of Heart Disease using Machine Learning Algorithms

Abstract

Data collection mechanism is designed and correlation analysis of those collected data. Machine learning data analysis is data analysis techniques that can be visually represent the knowledge embedded in dataset. All the simulations are performed in Spyder (Python 3.9) and other mandatory libraries system to process the patient data. The risk factors that cause heart disease are considered and predict and predict using some machine learning algorithms. Finally, it is designed to foresee the future health condition of the most heart patients based on their current health status. The best way to prevent such medical error is by reducing the reliability of memory and by improving the information access. The health related unsupervised data is used for finding hidden features that may indicate a disease state in patients. To predict heart disease, machine learning algorithm is used along with data analytics and visualization tool. In result section, with this dataset and algorithms clearly shows that prediction is accurate for heart disease patients