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A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data

Author

Listed:
  • Muhammad Noman Sohail

    (Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China)

  • Jiadong Ren

    (Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China)

  • Musa Uba Muhammad

    (Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China)

Abstract

The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.9.2) using 281 real-life health records of diabetes mellitus patients including males and females of ages>20 and <87, who were simultaneously suffering from other chronic disease symptoms, in Nigeria from 2017 to 2018. Updated plugins of K-mean and self-organizing map(SOM) machine learning algorithms were used to cluster the data class of mellitus type for initial clinical implications. The results of the K-mean assessment were built in 0.21 seconds with nine iterations for “type” and eight for “class” attributes. Out of 281 instances, 87 (30.97%) were classified as negative and 194 (69.03%) as positive in the testing on the Euclidean space plot. By assessment for Euclidean points, SOM discovered the search space in a more effective way, but K-mean positioning potencies are impulsive in convergence. This study is important for epidemiological disease diagnosis in countries with a high epidemic risk and low socioeconomic status.

Suggested Citation

  • Muhammad Noman Sohail & Jiadong Ren & Musa Uba Muhammad, 2019. "A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data," IJERPH, MDPI, vol. 16(9), pages 1-12, May.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:9:p:1581-:d:228597
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    Citations

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    Cited by:

    1. Sohail M. Noman & Jehangir Arshad & Muhammad Zeeshan & Ateeq Ur Rehman & Amir Haider & Shahzada Khurram & Omar Cheikhrouhou & Habib Hamam & Muhammad Shafiq, 2021. "An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method," IJERPH, MDPI, vol. 18(7), pages 1-11, April.
    2. Musa Uba Muhammad & Ren Jiadong & Noman Sohail Muhammad & Munawar Hussain & Irshad Muhammad, 2019. "Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases," IJERPH, MDPI, vol. 16(19), pages 1-15, September.
    3. Hyoji Ha & Jihye Lee & Hyunwoo Han & Sungyun Bae & Sangjoon Son & Changhyung Hong & Hyunjung Shin & Kyungwon Lee, 2019. "Dementia Patient Segmentation Using EMR Data Visualization: A Design Study," IJERPH, MDPI, vol. 16(18), pages 1-16, September.
    4. Musa Uba Muhammad & Ren Jiadong & Noman Sohail Muhammad & Bilal Nawaz, 2019. "Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach," IJERPH, MDPI, vol. 16(21), pages 1-11, October.

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