ANOVA-based Automatic Attribute Selection and a Predictive Model for Heart Disease Prognosis

TL;DR

This paper presents an ANOVA-based attribute selection method combined with domain knowledge to improve heart disease prognosis, demonstrating high accuracy on multiple datasets including a new dataset.

Contribution

It introduces a novel attribute selection technique using ANOVA and domain expertise, along with a new dataset for cardiovascular research.

Findings

Achieved 99.2% mean average accuracy

Achieved 97.9% mean average AUC

Validated on four benchmark datasets and a new dataset

Abstract

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%.