ANOVA-based Automatic Attribute Selection and a Predictive Model for Heart Disease Prognosis
This work addresses cardiovascular disease prediction for healthcare applications, but it appears incremental as it builds on existing machine learning approaches with a new dataset.
The paper tackles heart disease prognosis by proposing an ANOVA-based attribute selection method combined with domain knowledge, achieving a mean average accuracy of 99.2% and mean AUC of 97.9% on benchmark and new datasets.
Studies show that Studies that cardiovascular diseases (CVDs) are malignant for human health. Thus, it is important to have an efficient way of CVD prognosis. In response to this, the healthcare industry has adopted machine learning-based smart solutions to alleviate the manual process of CVD prognosis. Thus, this work proposes an information fusion technique that combines key attributes of a person through analysis of variance (ANOVA) and domain experts' knowledge. It also introduces a new collection of CVD data samples for emerging research. There are thirty-eight experiments conducted exhaustively to verify the performance of the proposed framework on four publicly available benchmark datasets and the newly created dataset in this work. The ablation study shows that the proposed approach can achieve a competitive mean average accuracy (mAA) of 99.2% and a mean average AUC of 97.9%.