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https://github.com/georgehanymilad/heart-disease-detection
Machine Learning Project
https://github.com/georgehanymilad/heart-disease-detection
anaconda classification classification-algorithm dataanalysis datascience kaggle machine-learning machine-learning-algorithms matplotlib numpy pandas python python3 seaborn
Last synced: about 9 hours ago
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Machine Learning Project
- Host: GitHub
- URL: https://github.com/georgehanymilad/heart-disease-detection
- Owner: GeorgeHanyMilad
- Created: 2024-07-01T11:32:20.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-07-14T13:50:02.000Z (4 months ago)
- Last Synced: 2024-07-14T18:02:04.773Z (4 months ago)
- Topics: anaconda, classification, classification-algorithm, dataanalysis, datascience, kaggle, machine-learning, machine-learning-algorithms, matplotlib, numpy, pandas, python, python3, seaborn
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/georgehanymilad/heart-disease-detection
- Size: 6.78 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Heart Disease Detection Model 💔
This repository contains the implementation of a machine learning model to detect heart disease based on various medical parameters.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Libraries](#libraries)
- [Steps](#steps)
- [Algorithms](#algorithms)
- [Finally](#finally)
## Introduction
The "Heart Disease Detection Model" is a predictive analytics project designed to identify the presence of heart disease in patients based on a variety of health metrics and diagnostic test results. Utilizing machine learning algorithms, the model analyzes input data such as age, gender, blood pressure, cholesterol levels, and other relevant medical information to assess the likelihood of heart disease.
This project is intended for healthcare professionals, including doctors, nurses, and medical researchers, to assist in early detection and diagnosis of heart conditions. It can also be beneficial for health institutions aiming to enhance preventive care, as well as for patients who want to understand their heart health risk factors.
## Features
The dataset contains the following features:
- **age:** Age of the patient
- **sex:** Sex of the patient
- **cp:** Type of chest pain
- **trestbps:** Resting blood pressure
- **chol:** Cholesterol level
- **fbs:** Fasting blood sugar level
- **restecg:** Resting electrocardiographic results
- **thalach:** Maximum heart rate achieved
- **exang:** Exercise-induced angina
- **oldpeak:** ST depression induced by exercise relative to rest
- **slope:** Slope of the peak exercise ST segment
- **ca:** Number of major vessels
- **thal:** Thalassemia (type of blood disorder)
- **target:** Presence of heart disease (Yes/No)
## Libraries
- numpy
- pandas
- matplotlib
- seaborn
- plotly
- sklearn
- xgboost
- sweetviz
- time
- warnings
## Steps
- Reading and Understand The Dataset
- Exploratory Data Analysis (EDA)
- Feature Engineering and Preprocessing
- Feature Selection
- Splitting the Data
- Model Selection and Training
- Model Evaluation
## Algorithms
- Logistic Regression (86.99 %)
- KNN (98.20 %)
- Support Vector Machine (87.89 %)
- Decision Tree (95.52 %)
- Random Forest (97.31 %)
- XGBoost (88.78 %)
- Naive Bayes (85.20 %)
## Finally
- ☛ You can express your opinion about this project through: [Kaggle Notebook](https://www.kaggle.com/code/georgehanymilad/heart-disease-detection)
- ☛ You can also try this project and benefit from it by: [Google Colab](https://colab.research.google.com/drive/177xrCGubfCDTkjNQHjYJfjYRwoJ_laFj?usp=sharing)
- ☛ For more projects and activities, follow me here: [My GitHub Profile](https://github.com/GeorgeHanyMilad)
- ☛ If you would to contact and chat with me: [Gmail](mailto:[email protected])