Predictive Analysis of Heart Disease using Machine Learning

Abstract

Heart disease, also known as cardiovascular disease, remains a substantial contributor to global mortality. This highlights the crucial necessity for implementing risk assessment strategies in a timely and effective manner, ensuring the prompt detection of this issue.. In this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Timely detection of this problem is very important for preventing patients before it because more damage. Timely analysis and risk assessment are important in managing and preventing this condition.

We explore the application of machine learning techniques for predicting heart disease by using diverse datasets and information of many patients. Machine Learning Algorithms Such as logistic regression and random forest can identify hidden patterns in data and information of patients and build predictive models for heart disease risk. These algorithms Consider different factors, including demographics, medical history , bloodwork and lifestyle habits , providing a more complete detail of individual risk of heart diseases.  Other than prediction Machine Learning algorithm can be used   in different ways such as identify subgroups of patients with specific risk profiles, allowing for personalized treatment strategies. After prediction we can start treatment planning based on predicted values.

In spite of all its potential. It’s important to know its limitations of machine learning in healthcare.

The accuracy of any model depend on completeness of  data and information  biases within this data can lead to not accurate and exact result we want.

This Study aims to provide insights into accuracy, interpretability, generalization of machine learning models in related to Heart Disease.