DeepAI AI Chat
Log In Sign Up

iCardo: A Machine Learning Based Smart Healthcare Framework for Cardiovascular Disease Prediction

by   Nidhi Sinha, et al.
University of North Texas
Malaviya National Institute of Technology Jaipur

The point of care services and medication have become simpler with efficient consumer electronics devices in a smart healthcare system. Cardiovascular disease is a critical illness which causes heart failure, and early and prompt identification can lessen damage and prevent premature mortality. Machine learning has been used to predict cardiovascular disease (CVD) in the literature. The article explains choosing the best classifier model for the selected feature sets and the distinct feature sets selected using four feature selection models. The paper compares seven classifiers using each of the sixteen feature sets. Originally, the data had 56 attributes and 303 occurrences, of which 87 were in good health, and the remainder had cardiovascular disease (CVD). Demographic data with several features make up the four groups of overall features. Lasso, Tree-based algorithms, Chi-Square and RFE have all been used to choose the four distinct feature sets, each containing five, ten, fifteen, and twenty features, respectively. Seven distinct classifiers have been trained and evaluated for each of the sixteen feature sets. To determine the most effective blend of feature set and model, a total of 112 models have been trained, tested, and their performance has been compared. SVM classifier with fifteen chosen features is shown to be the best in terms of overall accuracy. The healthcare data has been maintained in the cloud and would be accessible to patients, caretakers, and healthcare providers through integration with the Internet of Medical Things (IoMT) enabled smart healthcare. Subsequently, the feature selection model chooses the most appropriate feature for CVD prediction to calibrate the system, and the proposed framework can be utilised to anticipate CVD.


page 3

page 5

page 9

page 12

page 13

page 14


Analyzing the impact of feature selection on the accuracy of heart disease prediction

Heart Disease has become one of the most serious diseases that has a sig...

An Explainable Classification Model for Chronic Kidney Disease Patients

Currently, Chronic Kidney Disease (CKD) is experiencing a globally incre...

Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data

Clinical methods that assess gait in Parkinson's Disease (PD) are mostly...

HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems

The integration of Internet-of-Things and pervasive computing in medical...

Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients

Multiple organ failure (MOF) is a life-threatening condition. Due to its...

A Comprehensive Study of Radiomics-based Machine Learning for Fibrosis Detection

Objectives: Early detection of liver fibrosis can help cure the disease ...

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

Studies show that Studies that cardiovascular diseases (CVDs) are malign...