Key result
Naïve Bayes and XGBOOST classifiers predicted cardiovascular disease with the highest testing accuracies of 92.34% and 92.31%, respectively, outperforming other evaluated machine learning models.
Why the study?
High mortality from cardiovascular and chronic respiratory diseases is driven largely by the inability to forecast symptoms, prompting the development of machine learning models for early prediction and diagnosis.
Machine learning algorithms like Naïve Bayes and XGBOOST can accurately predict cardiovascular disease risk using standard clinical and lifestyle parameters.
Warrants prospective validation of ML classifiers for CVD prediction; leaves open their role in routine clinical risk assessment.
Nearly 19 million people die each year from cardiovascular and chronic respiratory diseases, which are a global threat. It is necessary to address the causes of these diseases because of the high death rate. The investigation uncovered a number of causes, but the inability to forecast these diseases symptoms is by far the most significant. In this work, we developed a method for anticipating these diseases crucial symptoms, which will aid in early disease diagnosis and allow patients to begin treatment. This research will introduce a new computational medicine research using machine learning (ML) paradigms to forecast cardiovascular disease (CVD). Data were processed by methods in sequence with various parameters. different models created that predicts CVD risk based on individual age, gender, ethnicity, body mass etc., and lifestyle factors. The research will also focus on performing complete comparison of ML models. We will apply Five ML based algorithems such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBOOST and Random Forest and evaluate these models on the basis of Training and Testing and also calculated the Presicion Recall and F1-Score for each model. Naïve Bayes and XGBOOST Classifier perform better with accuracy of 92.31 and 92.34 percent as compared to other models.
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Kanwal et al. (2023) studied Cardiovascular Disease (n=70,001). Machine learning algorithms (Naïve Bayes and XGBOOST) vs. Other machine learning models (Decision Tree, Random Forest, K-Nearest Neighbors) was evaluated on Prediction accuracy for cardiovascular disease. Naïve Bayes and XGBOOST classifiers predicted cardiovascular disease with the highest testing accuracies of 92.34% and 92.31%, respectively, outperforming other evaluated machine learning models.
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