Heart Disease Predictor Using ML with Python

Abstract

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM). Prediction and diagnosing of heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with the help of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

Introduction

The text discusses the importance of predicting heart disease using machine learning due to its high global mortality rate and growing healthcare impact. Heart disease is a leading cause of death worldwide, including in India, and early prediction is critical to reduce healthcare costs and improve patient outcomes. Traditional clinical approaches are often insufficient, so machine learning is used to analyze large, complex, and noisy medical datasets for more accurate diagnosis and risk prediction.

The literature review highlights several existing approaches for heart disease prediction using machine learning. Studies include methods such as data mining with WEKA, hybrid models like Random Forest–Linear Method (HRFLM), Support Vector Machines (SVM), Logistic Regression, and ensemble techniques. Many researchers use the Cleveland dataset and apply preprocessing, feature selection, and model optimization techniques. Hybrid and optimized models generally show improved accuracy compared to single algorithms, with reported results reaching above 90% in some cases. However, challenges remain in improving accuracy, handling missing data, and selecting optimal features.

The methodology describes a supervised machine learning framework using patient health attributes such as age, blood pressure, cholesterol, ECG results, chest pain type, and other clinical indicators. The system uses classification techniques to predict whether a patient has heart disease or not. Supervised learning is explained as training a model on labeled data to predict outcomes.

Key algorithms used include Random Forest, which combines multiple decision trees for higher accuracy and reduced overfitting, and K-Nearest Neighbour (KNN), which classifies data based on similarity to nearby data points.

Conclusion

The Heart Disease Prediction System demonstrates the effectiveness of machine learning in healthcare applications. By analyzing patient medical attributes, the system can accurately predict the likelihood of heart disease and assist healthcare professionals in decision-making. The integration of machine learning algorithms with a Streamlit web application creates a practical, efficient, and user-friendly healthcare prediction system. The project highlights the importance of AI-driven predictive analytics in improving healthcare accessibility, reducing diagnosis time, and supporting early disease detection. Although the system has certain limitations, it represents a significant step toward intelligent healthcare technologies and future AI-assisted medical systems.

References

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Copyright

Copyright © 2026 Sancharika Banerjee, Sukanta Seni, Nitesh Kumar Singh, Shamail Nabi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.