Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria. Issue 4 (9th July 2019)

• Record Type: Journal Article
• Authors: Uba, Muhammad Musa; Jiadong, Ren; Sohail, Muhammad Noman; Irshad, Muhammad; Yu, Kaifei
• Is Part Of: Healthcare technology letters. Volume 6:Issue 4(2019)
• Page Start: 98
• Page End: 102
• Publication Date: 2019-07-09
• Subjects: regression analysis -- pattern classification -- diseases -- medical disorders -- medical computing -- data mining -- patient treatment -- medical diagnostic computing -- patient diagnosis -- biomedical measurement; testing sets -- training data -- best‐fitted model converges -- predicted model -- diabetic patients -- chronic diseases -- data mining process -- diabetes mellitus based model -- northwestern part -- Nigeria -- diabetes mellitus model data mining based approaches -- dataset -- seven northwestern states -- primary sources -- secondary sources -- diabetic mellitus -- hospital data -- DM techniques -- confusion matrix -- correlation coefficient; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2018.5111
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best‐fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.