PMID- 32211144
OWN - NLM
STAT- MEDLINE
DCOM- 20210517
LR  - 20210517
IS  - 2040-2309 (Electronic)
IS  - 2040-2295 (Print)
IS  - 2040-2295 (Linking)
VI  - 2020
DP  - 2020
TI  - Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine 
      Learning Algorithms.
PG  - 4984967
LID - 10.1155/2020/4984967 [doi]
LID - 4984967
AB  - 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, naive 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.
CI  - Copyright (c) 2020 Theyazn H.H Aldhyani et al.
FAU - Aldhyani, Theyazn H H
AU  - Aldhyani THH
AUID- ORCID: 0000-0003-1822-1357
AD  - Department of Computer Sciences and Information Technology, King Faisal 
      University, Al-Hasa 31982, Saudi Arabia.
FAU - Alshebami, Ali Saleh
AU  - Alshebami AS
AD  - Department of Administrative and Financial Sciences, King Faisal University, 
      Al-Hasa 31982, Saudi Arabia.
FAU - Alzahrani, Mohammed Y
AU  - Alzahrani MY
AUID- ORCID: 0000-0002-9726-6088
AD  - Department of Computer Sciences and Information Technology, Albaha University, 
      Albaha 65527, Saudi Arabia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200309
PL  - England
TA  - J Healthc Eng
JT  - Journal of healthcare engineering
JID - 101528166
SB  - IM
MH  - *Algorithms
MH  - Bayes Theorem
MH  - *Chronic Disease
MH  - Cluster Analysis
MH  - Female
MH  - Humans
MH  - *Machine Learning
MH  - Male
MH  - Mass Screening/*standards
MH  - Public Health
MH  - Support Vector Machine
MH  - United States
PMC - PMC7085388
COIS- The authors declare no conflicts of interest.
EDAT- 2020/03/27 06:00
MHDA- 2021/05/18 06:00
PMCR- 2020/03/09
CRDT- 2020/03/27 06:00
PHST- 2019/10/08 00:00 [received]
PHST- 2020/01/02 00:00 [revised]
PHST- 2020/01/18 00:00 [accepted]
PHST- 2020/03/27 06:00 [entrez]
PHST- 2020/03/27 06:00 [pubmed]
PHST- 2021/05/18 06:00 [medline]
PHST- 2020/03/09 00:00 [pmc-release]
AID - 10.1155/2020/4984967 [doi]
PST - epublish
SO  - J Healthc Eng. 2020 Mar 9;2020:4984967. doi: 10.1155/2020/4984967. eCollection 
      2020.