Cardiovascular diseases prediction by machine learning incorporation with deep learning

Abstract

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree

“… 15 ML algorithms have proven to be highly effective predictors, surpassing classic statistical models in capturing complex interactions and non-linear relationships between variables and outcomes. 16 In ML terminology, supervised and unsupervised learning are two fundamental approaches used by data scientists in classification and clustering the study subjects. Supervised learning requires labeled data and focuses on predicting specific outcomes, while unsupervised learning aims to explore data patterns and structures without labeled examples.…”

“… 26 In 2023, Subramani et al investigated the integration of deep learning with ML methods including SVM, KNN, LR, XGBoost, NB, LR, and DT. 16 In another study in 2022, the researchers employed the multi-layer perceptron and KNN techniques for detecting CVD patients using data publicly available in the University of California Irvine repository.…”

Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis

“…Subramani et al [3] study presented a suite of ML models designed to address a specific problem, incorporating diverse data observation methods and training procedures from various algorithms. To validate the efficacy of the strategy, the Heart Dataset was amalgamated with other classifiers.…”

Machine Learning Models for Cardiovascular Disease Prediction: A Comparative Study

“…The precise origins of cardiovascular disease (CVD) continue to evade us, though its correlation with escalated mortality rates and substantial morbidity and disability is broadly acknowledged. Sivakannan Subramani's investigation presents an assortment of machine learning models purportedly aimed at tackling this conundrum [4]. Allegedly, these models are crafted to amalgamate various data observation techniques and training protocols across an array of algorithms.…”