Heart-Disease-Prediction

The heart is one of the most important organs in the human body. It pumps blood throughout the body, delivering oxygen and nutrients to cells and tissues. Diagnosing heart disease is crucial because early detection improves treatment outcomes. It can reduce mortality and improve the quality of life for people.

Project Overview

This project aims to predict heart disease using machine learning models and ensemble methods. The goal is to build a model that can accurately predict the presence of heart disease based on various medical attributes. Evaluations are done using the Cleveland dataset.

Table of Contents

• Project Overview
• Data
• Models
• Ensemble Method
• Results
• Applications
• Contact

Data

The dataset used for this project is the Heart Disease dataset from the UCI Machine Learning Repository. It includes various medical attributes such as age, sex, blood pressure, cholesterol levels, etc. Link to dataset: https://archive.ics.uci.edu/dataset/45/heart+disease

Models

The following machine learning models are used in this project:

• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)

Ensemble Method

An ensemble method is used to combine the predictions of multiple models to improve accuracy and robustness. Using the Soft Voting method, the three best-performing ML models are used for making the final prediction. The structure is as follows:

Results

The results are summarized below:

These metrics indicate that the ensemble model outperforms the base models in predicting heart disease. It can balance both precision (correctly identifying those with the disease) and recall (correctly identifying those without the disease).

Applications

The heart disease prediction model has several potential applications in healthcare:

• Early Diagnosis: Assists healthcare providers in identifying patients at high risk of heart disease, allowing for early intervention.
• Personalized Treatment Plans: Helps tailor treatment plans based on the predicted risk, improving patient outcomes.
• Timely Alerts: Provide patients with timely alerts based on their health data, encouraging proactive health management.
• Continuous Monitoring: Enables continuous monitoring of patients with a history of heart disease, ensuring they receive appropriate care.

Contact

For any questions or suggestions, please contact on LinkedIn: https://www.linkedin.com/in/siddiquizainab/