TY  - EJOU
AU  - Hussain, Adil 
AU  - Aslam, Ayesha 

TI  - Cardiovascular Disease Prediction Using Risk Factors: A Comparative Performance Analysis of Machine Learning Models
T2  - Journal on Artificial Intelligence

PY  - 2024
VL  - 6
IS  - 1
SN  - 2579-003X

AB  - The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, including Logistic Regression, Random Forest, Decision Tree, Extra Trees classifier, Support Vector Machine (SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), GaussianNB, and Multilayer Perceptron (MLP). The machine learning models are applied to a concrete dataset acquired from Kaggle. The models underwent training using a dataset that was partitioned into an 80:20 ratio. Machine learning model evaluation involves the utilization of performance measurements such as Accuracy, Precision, Recall, and ROC curves. An exhaustive evaluation is carried out to gauge the efficacy of the models.
KW  - Cardiovascular disease; heart disease; disease prediction; machine learning; performance analysis

DO  - 10.32604/jai.2024.050277