This repository contains the implementation of a machine learning model to detect heart disease based on various medical parameters.
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.
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)
- pandas
- matplotlib
- seaborn
- plotly
- sklearn
- warnings
- Reading the Data
- Exploratory Data Analysis (EDA)
- Feature Engineering and Preprocessing
- Splitting the Data
- Model Selection and Training
- Model Evaluation
- Logistic Regression (87.00 %)
- Support Vector Machine (87.89 %)
- Decision Tree (95.52 %)
- Random Forest (97.31 %)
If you have any feedback, please reach out to me at Kaggle Notebook