Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria.

Clicks: 414
ID: 53002
2019
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
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.
Reference Key
uba2019datahealthcare Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Uba, Muhammad Musa;Jiadong, Ren;Sohail, Muhammad Noman;Irshad, Muhammad;Yu, Kaifei;
Journal healthcare technology letters
Year 2019
DOI
10.1049/htl.2018.5111
URL
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

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.