Heart Disease Clinical Dataset for Artificial Intelligence and Healthcare Research

Description

This dataset contains clinical and demographic information collected from 918 patients and is intended for research on heart disease prediction and classification using machine learning techniques. The dataset includes 11 input features and 1 target variable (HeartDisease). The attributes represent patient demographics, clinical measurements, electrocardiographic findings, and exercise-related indicators that are commonly associated with cardiovascular health. The features include Age, Sex, Chest Pain Type, Resting Blood Pressure, Cholesterol Level, Fasting Blood Sugar, Resting Electrocardiogram Results, Maximum Heart Rate Achieved, Exercise-Induced Angina, ST Depression (Oldpeak), and ST Slope. The target variable, HeartDisease, indicates the presence (1) or absence (0) of heart disease. This dataset can be used for various healthcare analytics tasks, including binary classification, risk prediction, feature importance analysis, model benchmarking, and explainable artificial intelligence (XAI) studies. It is suitable for evaluating machine learning and deep learning algorithms aimed at supporting early diagnosis and decision-making in cardiovascular healthcare. Keywords: Heart Disease, Cardiovascular Disease, Machine Learning, Healthcare Analytics, Clinical Data, Predictive Modeling, Medical Diagnosis, Artificial Intelligence, Classification Dataset, Risk Assessment.

Steps to reproduce

1. Download the dataset file. 2. Load the dataset into a data analysis environment such as Python, R, MATLAB, or WEKA. 3. Perform data preprocessing, including handling missing values (if any), encoding categorical variables, and feature scaling as required. 4. Split the dataset into training and testing subsets. 5. Train machine learning models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, XGBoost, or other classification algorithms. 6. Evaluate model performance using metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC. 7. Compare the performance of different models and analyze the importance of clinical features in heart disease prediction. 8. Reproduce the results by using the same preprocessing steps, train-test split strategy, and evaluation metrics.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

Categories

Licence