A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

Abstract

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on “Early Heart Disease Prediction using Boosting Techniques”, this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.

Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

“…As the authors claimed, the eventual goal of this work has been to enhance clinical practice by providing doctors with a new instrument to determine a patient's CVD prognosis. The primary objective in the study by Nissa et al 41 has been to identify individuals at risk of developing CVDs and eventually provide healthcare professionals County phenotypes associated with premature cardiovascular mortality have been identified in Dong et al 42 and their geographic distributions have been investigated using machine learning approaches as well as geographic information systems. The authors conclude that interventions to reduce premature cardiovascular mortality should be targeted to geographic areas with high-risk phenotypes of premature cardiovascular mortality.…”

“…35,40,[45][46][47][48]54,[60][61][62][63]67,68 The second most commonly encountered comparative approach involves utilizing non-linear and ensemble methods. 41,55,68 Guimbaud et al 39 have exploited linear and ensemble algorithms and Jalili et al 65 have focused on linear and non-linear algorithms. Another comparative approach involves selecting more than one algorithm but within a specific family of models.…”

“…Proceeding with the remaining 30% of pertinent literature that does not conduct performance comparison whatsoever, a part of research opted for a non-linear algorithm, 42,43,56,57 while another part has focused on an ensemble algorithm of their selection. 50,51,58,59 As it becomes obvious from Figure 4 ensemble algorithms are the most popular choice encountered in pertinent literature, 35,[39][40][41][44][45][46][47][48][49][50][51][52][53][54][55]58,[60][61][62][63][66][67][68] closely followed by non-linear algorithms. 35,[41][42][43][44][45][46][47][48][49]54,59,60,62,[64]…”

“…50,51,58,59 As it becomes obvious from Figure 4 ensemble algorithms are the most popular choice encountered in pertinent literature, 35,[39][40][41][44][45][46][47][48][49][50][51][52][53][54][55]58,[60][61][62][63][66][67][68] closely followed by non-linear algorithms. 35,[41][42][43][44][45][46][47][48][49]54,59,60,62,[64][65][66][67][68][69][70][71]...…”

“…Regarding socio-economic factors, a wide range of variables are used, primarily reflecting income, education levels, economic status and/or the subject's occupation. 39,41,47,48,51,55,67,71 Less often, extra variables such as lack of private health insurance, home ownership, housing type or severe housing cost burden are also considered. 42,44,58,66,70 In terms of lifestyle factors the focus is predominantly on health-related habits and adopted dietary, physical activity and sleep patterns/quality.…”