Abstract
Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.
Highlights
In a conventional classification model, the classification strategy identifies and selects the best classifier on the basis of experimental assessment with various individual classifiers
The first step in Rough Set Rule-based Multitude Classifier (RS-RMC) framework is the construction of Rough Set Feature Selection (RSFC) to reduce the complexity by minimizing the redundant disease features contained within the set of feature patterns
By applying Naïve Bayes Classifier algorithm, efficient identification of classes through maximum posterior hypothesis is evaluated that helps in measuring the relationship between disease features and significantly improves the disease prediction rate by 18.52% and 38.74% compared to Prevention and Potential Management of Ventricular Arrhythmias (PPM-VA) and PE-SSTD respectively
Summary
In a conventional classification model, the classification strategy identifies and selects the best classifier on the basis of experimental assessment with various individual classifiers. Prediction of Events using Spatio Spectro Temporal Data (PE-SSTD) [2] provided with a case study on stroke resulting in the accuracy of the disease being diagnosed. Both the above methods lack the class accuracy of disease diagnosis with the increase in the feature. In accordance with the above-mentioned advantages of both disease classification and disease diagnosis, in this paper, a new framework called Rough Set Rule-based Multitude Classifier (RS-RMC) is proposed to increase the disease prediction rate and efficiency of the classification accuracy of disease being diagnosed
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