Abstract
A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.
Highlights
The devastating effects of chronic diseases on the health and wellbeing of populations all over the globe are becoming worrisome
The present study aims to evaluate the application of a hybrid approach where both the principal component analysis (PCA) and ReliefF are combined with support vector machine (SVM) classifier for achieving data dimensionality reduction and data classification improvements in disease prediction
The classification performance of the proposed system is evaluated at the end of proposed feature selection via the ReliefF-PCA method
Summary
The devastating effects of chronic diseases on the health and wellbeing of populations all over the globe are becoming worrisome. In 2017, WHO reports that approximately 205 million women suffer from diabetes worldwide (WHO, 2017) These effects are likely to be exacerbated with the rapidly aging populations growing in many countries all over the world. It is essential, in times such as these, that efficient diagnostic mechanisms be developed to render best possible remedy to patients inflicted with various chronic diseases in the least amount of time to sustain a decent level of population health and wellbeing in the developed, developing and under-developing countries of the world. SVM is based on finding the maximum-margin hyper-plane for the separation of two classes as wide as possible. Feature selection eliminates the attributes that are least significant to a particular disease
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