Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria.
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 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.
Keywords
Data mining
Nigeria
regression analysis
diseases
chronic diseases
correlation coefficient
dataset
patient diagnosis
dm techniques
best-fitted model converges
biomedical measurement
confusion matrix
data mining process
diabetes mellitus based model
diabetes mellitus model data mining based approaches
diabetic mellitus
diabetic patients
hospital data
medical computing
medical diagnostic computing
medical disorders
northwestern part
patient treatment
pattern classification
predicted model
primary sources
secondary sources
seven northwestern states
testing sets
training data
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53002
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10.1049/htl.2018.5111
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Scimatic Chain (ID: 481)
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