A Novel Machine Learning Based Probabilistic Classification Model for Heart Disease Prediction
- 1 March 2022
- journal article
- Published by American Scientific Publishers in Journal of Medical Imaging and Health Informatics
- Vol. 12 (3), 221-229
- https://doi.org/10.1166/jmihi.2022.3940
Abstract
Cardiovascular disease (CVD) is most dreadful disease that results in fatal-threats like heart attacks. Accurate disease prediction is very essential and machine-learning techniques contribute a major part in predicting occurrence. In this paper, a novel machine learning based model for accurate prediction of cardiovascular disease is developed that applies unique feature selection technique called Chronic Fatigue Syndrome Best Known Method (CFSBKM). Each feature is ranked based on the feature importance scores. The new learning model eliminates the most irrelevant and low importance features from the datasets thereby resulting in the robust heart disease risk prediction model. The multi-nominal Naive Bayes classifier is used for the classification. The performance of the CFSBKM model is evaluated using the Benchmark dataset Cleveland dataset from UCI repository and the proposed models out-perform the existing techniques.Keywords
This publication has 27 references indexed in Scilit:
- Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification ApproachProceedings of the IEEE, 2018
- Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq DataPublished by Springer Science and Business Media LLC ,2017
- CEP4HFP: Complex Event Processing for Heart Failure PredictionIEEE Transactions on Nanobioscience, 2017
- Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosisPhysica A: Statistical Mechanics and its Applications, 2017
- Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain FeaturesIEEE Access, 2017
- Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithmComputer Methods and Programs in Biomedicine, 2017
- Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classificationBiomedical Signal Processing and Control, 2017
- Predicting sample size required for classification performanceBMC Medical Informatics and Decision Making, 2012
- Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural NetworksInternational Journal of Computer Applications, 2011
- Missing data imputation using statistical and machine learning methods in a real breast cancer problemArtificial Intelligence in Medicine, 2010