Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet

ABSTRACT

Aiming at early detection and accurate prediction of cardiovascular disease (CVD) to reduce mortality rates, this study focuses on the development of an intelligent predictive system to identify individuals at risk of CVD. The primary objective of the proposed system is to combine deep learning models with advanced data mining techniques to facilitate informed decision-making and precise CVD prediction. This approach involves several essential steps, including the preprocessing of acquired data, optimized feature selection, and disease classification, all aimed at enhancing the effectiveness of the system. The chosen optimal features are fed as input to the disease classification models and into some Machine Learning (ML) algorithms for improved performance in CVD classification. The experiment was simulated in the Python platform and the evaluation metrics such as accuracy, sensitivity, and F1_score were employed to assess the models’ performances. The ML models (Extra Trees (ET), Random Forest (RF), AdaBoost, and XG-Boost) classifiers achieved high accuracies of 94.35%, 97.87%, 96.44%, and 99.00%, respectively, on the test set, while the proposed CardioVitalNet (CVN) achieved 87.45% accuracy. These results offer valuable insights into the process of selecting models for medical data analysis, ultimately enhancing the ability to make more accurate diagnoses and predictions.

KEYWORDS:

• Cardiovascular Disease
• Classification
• CardioVitalNet
• Machine Learning
• Shapley

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (No. 62072074), the Sichuan Science and Technology Innovation Platform and Talent Plan (No. 2022JDJQ0039), and the CCF-Baidu Open Fund (No.202312). We also acknowledge the support from Amos I Ugwoke (Rev), Onyishi Akabuisiyi Obimo Elder Moses Ejiyi, and his entire family.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset used in this analysis is a synthesis of five different heart disease datasets sourced from various locations. The datasets include Cleveland, Hungarian, Switzerland, Long Beach VA, and Stalog (Heart), all of which are available at https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [62072074]; Sichuan Science and Technology Innovation Platform and Talent Plan [2022JDJQ0039].