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. 2020 Mar 9:2020:4984967.
doi: 10.1155/2020/4984967. eCollection 2020.

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Theyazn H H Aldhyani  1 , Ali Saleh Alshebami  2 , Mohammed Y Alzahrani  3
Affiliations

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Theyazn H H Aldhyani et al. J Healthc Eng. .

Abstract

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people's lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Proposed model.
Figure 2
Figure 2
Sample of ambiguous objects.
Figure 3
Figure 3
Snapshot of output RKM algorithm.
Figure 4
Figure 4
Comparison results of existing naïve Bayes classifier and naïve Bayes using RKM algorithm for diabetic diseases.
Figure 5
Figure 5
Comparison results of existing SVM classifier and SVM using RKM algorithm for diabetic diseases.
Figure 6
Figure 6
Comparison results of existing random forest classifier and random forest using RKM algorithm for diabetic diseases.
Figure 7
Figure 7
Comparison results of existing KNN classifier and KNN using RKM algorithm for diabetic diseases.
Figure 8
Figure 8
Comparison of results of existing naïve Bayes classifier and naïve Bayes using RKM algorithm for kidney diseases.
Figure 9
Figure 9
Comparison of results of existing SVM classifier and SVM using the RKM algorithm for kidney diseases.
Figure 10
Figure 10
Comparison of results of existing random forest classifier and random forest using the RKM algorithm for kidney diseases.
Figure 11
Figure 11
Comparison results of existing KNN classifier and KNN using the RKM algorithm for kidney diseases.
Figure 12
Figure 12
Comparison of results of the existing naïve Bayes classifier and naïve Bayes using the RKM algorithm for cancer disease.
Figure 13
Figure 13
Comparison of results of the existing SVM classifier and SVM using the RKM algorithm for cancer disease.
Figure 14
Figure 14
Comparison of results of the existing random forest classifier and random forest using the RKM algorithm for cancer disease.
Figure 15
Figure 15
Comparison of results of the existing KNN classifier and KNN using the RKM algorithm for cancer disease.
See this image and copyright information in PMC

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