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https://github.com/chandkund/predicting-heart-disease

Welcome to the Heart Disease Prediction project! 🩺 This project focuses on developing a predictive model to assess heart disease risk based on health indicators like age, cholesterol levels, and blood pressure. By analyzing these features, we aim to create an effective tool for early diagnosis and heart disease prevention
https://github.com/chandkund/predicting-heart-disease

machine-learning matplotlib numpy pandas python seaborn sklearn

Last synced: 15 days ago
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Welcome to the Heart Disease Prediction project! 🩺 This project focuses on developing a predictive model to assess heart disease risk based on health indicators like age, cholesterol levels, and blood pressure. By analyzing these features, we aim to create an effective tool for early diagnosis and heart disease prevention

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README 

        
# ❤️ Predicting Heart Disease

## Overview

Welcome to the Heart Disease Prediction project! 🩺 This project aims to develop a predictive model that can assess the risk of heart disease based on various health indicators. By analyzing features such as age, cholesterol levels, and blood pressure, we strive to create an effective model to help in early diagnosis and prevention of heart disease.



## Dataset Description

The dataset contains various health metrics that are known to influence the likelihood of heart disease. Here are the key features:



- Age: Age of the patient.

- Sex: Gender of the patient (1 = male, 0 = female).

- Chest Pain Type: Type of chest pain experienced.

- Resting Blood Pressure: Blood pressure in mm Hg on admission to the hospital.

- Cholesterol: Serum cholesterol in mg/dl.

- Fasting Blood Sugar: Whether fasting blood sugar > 120 mg/dl (1 = true, 0 = false).

- Resting ECG: Results of the resting electrocardiogram.

- Max Heart Rate: Maximum heart rate achieved.

- Exercise Induced Angina: Whether exercise-induced chest pain is present (1 = yes, 0 = no).

- ST Depression: Depression induced by exercise relative to rest.

- Target: The presence of heart disease (1 = yes, 0 = no).

# Project Steps



## Data Exploration:



Analyzed the dataset to understand the distribution of features.

Visualized relationships between key features and heart disease risk.

## Data Preprocessing:



Handled missing values and scaled the numerical features.

Encoded categorical variables and performed feature selection.

## Model Building:



Developed and compared models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Neural Networks.

Tuned hyperparameters to optimize model performance.

## Model Evaluation:



Evaluated models using accuracy_score

Selected the most effective model for heart disease prediction.

## Results

The final model achieved an accuracy of 82%, demonstrating strong predictive power in identifying individuals at risk of heart disease.



## Conclusion

This project underscores the potential of machine learning in healthcare, particularly for early diagnosis and prevention of heart disease. The insights gained could contribute to more personalized healthcare strategies.



## How to Run

Clone the repository.

Install the required dependencies: `pip install -r requirements.txt`

Run the Jupyter Notebook to see the analysis and predictions