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Author(s): Idris Aliyu Kankara.

Volume/Issue: Volume 5 , Issue 1 (2025)

Abstract:

Diabetes Mellitus (DM) is a chronic disorder affecting more than 300 million people world-wide and it needs urgent attention. Selecting of significant risk factors (SRFs) and their contributions to the risk of the disease is the key for early detection of the disease. The aim of this paper is to use Backward Stepwise Feature Selection Method (BSFSM) to select the SRFs and their contribution to the risk of DM and Kappa statistic value (KSV) to evaluate the model performance. Dataset consists of 400 patients with demographic, clinical, lifestyle and dietary risk factors were collected from General Hospital Kaura Namoda, Zamfara State, Nigeria from 2019 to 2023 by checking file of patients suffering from DM. The results obtained revealed that BSFSM retained twelve (12) SRFs namely Blood Glucose level (BGL), High Body Mass Index (BMI), Family History of DM (FHDM), Preference for Sweet Food (PSWF), Age, Lack of Physical Activity (LPA), Blood Pressure (BP), Red Meat (RM), Refined Carbs (RC), Energy Drink (ED), White Rice (WR) and Processed Meat (PM) and remove two (2) Non-SRFs Sex and Preference for Salty Food (PSF). The SRFs contributed 85.40%, 51.34%, 55.72%, 68.23%, 57.50% 29.96% , 66.18%, 41.42%, 12.20%, 18.65%, 29.76% and 10.11% to the risk of DM respectively. Similarly, the Non-SRFs contributed 0.98% and 1.16% to the risk of DM. The MLP model detected 98.6% DM patients in the training set, 96.3% in the validation set and 92.9% for test set. 97.8% Non-DM patients in the training set, 93.9% in the validation set and 93.8% for the test set. The KSV of the model was 0.94 and it was capable of distinguishing between DM and Non-DM patients. This paper demonstrated that BSFSM was capable of selecting the SRFs and their contributions and KSV adequately evaluate the performance of the model

Keywords:

Diabetes Mellitus, Backpropagation, Detection, Artificial Neural Network, Kappa Statistic Value.

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