Nora Ibrahem Alghurair et al, International Journal of Computer Science and Mobile Computing, Vol.9 Issue.6, June- 2020, pg. 132-140
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International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
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IJCSMC, Vol. 9, Issue. 6, June 2020, pg.132 – 140
GENERIC FRAMEWORKS FOR SVM, ANN,
LGBM, AND LR ALGORITHMS
Nora Ibrahem Alghurair¹; Mohammad A. Mezher²
¹College of Postgraduate Studies & Scientific Research, Fahad Bin Sultan University, Tabuk, KSA
²College of Computing, Fahad Bin Sultan University, Tabuk, KSA
1
noraibrahem018@gmail.com;
2
mmezher@fbsu.edu.sa
Abstract— World Health Organization describes diabetes as a multiple etiological metabolic condition defined by
persistent hyperglycemia with anomalies in glucose, lipid and protein metabolism triggered by insulin secretion
deficiencies, lipid or both. Diabetes is one of the 21st century's most daunting health problems in the world, and affects
over 425 million people. Data mining is one of the major techniques which develops and supports medical data research.
The aim of this research was to establish a diagnostic framework for diabetes. The PIDD used to function description.
Two generic frameworks have been proposed in the study. The first framework uses the ANN technique and fed
production to SVM which yields the diagnostic result. With this generic framework, five experiments are carried out and
the highest accuracy achieved was 81.8%. The second framework employs an ensemble of majority voting techniques
which combines LGBM, SVM, and LR. The generic framework was right at 87.9 per cent. The frameworks presented in
contrast with other state-of-the-art solutions, and it found that the second solution is the better one.
Keywords— Neural Networks, LGBM, SVM, LR, Diabetes, PIMA Indian, UCI
I. INTRODUCTION
The World Health Organization (WHO) describes diabetes as a multiple etiological metabolic condition
defined by persistent hyperglycemia with glucose, lipid and protein metabolism abnormalities triggered by
deficiencies in insulin secretion, lipid, or both (Lorenzo et al.,2007). Nearly half of the patients with diabetes
was affected by heredity factor, which is known to be a significant characteristic of diabetes. The pathologic
causes of diabetes are the decreased insulin output leading to pancreatic failure. There are two forms of Diabetes.
β-cells are type 1 diabetes mellitus pathogenesis, which is impaired by pancreatic secretions, blocking the
lowering of blood glucose level. The impairment of insulin resistance and insulin secretion, the pathogeneses of
type 2 diabetes mellitus are known to be diabetes based on non-insulin (Muller et al.,2005).
In under fifty years, the incidence of this disorder has risen five-fold. This gradual increase is due to different
factors. World population growth, a rise in life expectancy for diabetics, a rise in pregnancy among diabetic
mothers, an increase in obesity, an increase in intake of processed sugars. Along with other factors that can
serve as a trigger such as sedentary lifestyle, diets high in saturated fat and proteins, reduced fiber intake, a food
poor in complex carbohydrates and vitamin E, unnecessary stress, Tobacco that may promote the resistance to
insulin (Vlassara & Uribarri.,2014).
Diabetes is one of the world's most daunting health problems of the 21st century and impacts more than 425
million individuals. The most prevalent type 2 diabetes mellitus triggered by insulin resistance is the most
severe form of diabetes accounting for 90-95 cases led by type 1 diabetes mellitus accounting for diabetes
becoming the world's fifth leading cause of disease death (Wild et al.,2004).
Diabetes is becoming a significant global problem that is viewed as a red threat to human health. Diabetes is
linked with serious symptoms and a variety of adverse reactions of all sorts including lack of hearing, impaired
immune function, cardiac problems, skin disorders, renal dysfunction, nerve impairment and even disruption to