The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Data Mining techniques can be used for disease prediction. In this research, the classification based data mining techniques are applied to healthcare data. This research focuses on the prediction of heart disease using three classification techniques namely Decision Trees, Naïve Bayes and K Nearest...
Many researchers make use of machine learning (ML) and/or rule-based (RB) methods to infer the mental state of an individual based on HRV. The HRV can be estimated using a variety of physiological measures, including heart rate, galvanic skin response, body temperature, and blood pressure. ...
We recruited 64 patients with prodromal AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579...
Heart disease prediction system using Correlation Based Feature Selection with Multilayer Perceptron approach. Cardiac disease prediction helps physicians to make accurate recommendations on the treatment of the patients. The use of machine learning (ML) is one of the solution for recognising heart disease...
Machine learning when implemented in health care is capable of early and accurate detection of disease. In this work, the arising situations of Heart Disease illness are calculated. Datasets used have attributes of medical parameters. The datasets are been processed in python using ML Algorithm i....
Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective...
Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While...
Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837-1847. doi:10.1161/01.CIR.97.18.1837PubMedGoogle ScholarCrossref 2. Conroy RM, Pyörälä K, Fitzgerald AP, et al; SCORE Project Group. Estimation of ten-year risk of fatal ...
Machine learning (ML), a branch of computer science, incorporates numerous variables using algorithms to identify their nonlinear relationships and complex interactions [3]. ML algorithms have been explored for their ability to predict heart failure [4], arrhythmia [5], postsurgical mortality [6], ...