Version 1
: Received: 14 June 2023 / Approved: 14 June 2023 / Online: 14 June 2023 (11:38:53 CEST)
How to cite:
Chellappan, D.; Rajaguru, H. Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance. Preprints2023, 2023061038. https://doi.org/10.20944/preprints202306.1038.v1
Chellappan, D.; Rajaguru, H. Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance. Preprints 2023, 2023061038. https://doi.org/10.20944/preprints202306.1038.v1
Chellappan, D.; Rajaguru, H. Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance. Preprints2023, 2023061038. https://doi.org/10.20944/preprints202306.1038.v1
APA Style
Chellappan, D., & Rajaguru, H. (2023). Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance. Preprints. https://doi.org/10.20944/preprints202306.1038.v1
Chicago/Turabian Style
Chellappan, D. and Harikumar Rajaguru. 2023 "Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance" Preprints. https://doi.org/10.20944/preprints202306.1038.v1
Abstract
Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).
Keywords
Type II Diabetes Mellitus; Machine Learning; Prediction; Dimensionality Reduction; Classifiers
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
