Revolutionizing cardiovascular disease classification through machine learning and statistical methods

Clinical Snapshot

Key Findings

• P Value: Statistical significance achieved via Friedman-Nemenyi test

• Effect Size: Extra Tree Classifier: superior accuracy and precision; XGBoost: superior recall, kappa, and F1 scores

• Primary Outcome: CVD classification accuracy using machine learning models

• Nnt Or Sensitivity: Sensitivity and specificity not reported; only precision and recall metrics provided

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and validation in clinical settings Would require TGA approval for clinical use; no current PBS implications; needs validation against Australian CVD populations and RACGP guidelines Limited applicability - based on retrospective datasets rather than prospective clinical populations

Abstract

BACKGROUND: Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques. METHOD: In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions. RESULTS: The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.

References

1. 1.Behera, T. K., Sathia, S., Panigrahi, S., & Naik, P. K. (2024). Revolutionizing cardiovascular disease classification through machine learning and statistical methods. Journal of Biopharmaceutical Statistics. https://doi.org/10.1080/10543406.2024.2429524