An Optimal Framework for Intelligent Prediction of Prediabetes and Type-2 Diabetes Using Genomic Data

Article Sidebar

Published: Apr 30, 2025
Keywords:
Genomic data Functional link neural approach Normalization Chimp optimization Feature selection

Main Article Content

Sreenivas Pratapagiri*
Research Scholar, School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India, 506371.
Shanker Chandre
Assistant Professor, Supervisor, School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India, 506371.
Dr. Balaji Maram
Professor, Supervisor,School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India, 506371.

Abstract

The most important study in medical research is the prediction of type 2 diabetes in patients. As far as type 2 diabetes prediction models go, there are a number of options. Nevertheless, due to subpar quality, the desired outcome was not achieved. Nan characteristics in the gene data make type 2 diabetes prediction as complicated as possible. Scores for both performance and prediction were low due to the flaws. Consequently, the plan is to create a new method for predicting type 2 diabetes using chimpanzees and functional link neural architecture (CbFLNA). Methods such as feature selection, categorization, and gene expression have been carried out as part of the pre-processing methodology. The first steps were to import the genomic database, preprocess the data, and extract the meaningful features. Next, sort the individuals' illnesses according to the likelihood that they have type 2 diabetes. When all was said and done, the model's performance was evaluated, and it achieved a very high accuracy score in the prediction.

Article Details

Issue
Vol. 167 No. A2 (S) (2025): Technologies and Their Effects on Real-Time Social Development
Section
Articles

References

Shojima, Nobuhiro, and Toshimasa Yamauchi. "Progress in the genetics of type 2 diabetes and diabetic complications." Journal of Diabetes Investigation (2023).

Jin, Heejin, et al. "Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study meta-analysis." BMC Medicine 21.1 (2023): 16.

Rastogi, Rashi, and Mamta Bansal. "Diabetes prediction model using data mining techniques." Measurement: Sensors 25 (2023): 100605.

Elashi, Asma A., et al. "The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study." International Journal of Molecular Sciences 24.1 (2023): 130.

Ghalwash, Mohamed, et al. "Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study." The Lancet Child & Adolescent Health (2023).

Ahmadizar, Fariba, et al. "Association between Arterial Stiffness/Remodeling and New-onset Type 2 Diabetes Mellitus in General Population." Diabetes Research and Clinical Practice (2023): 110237.

Khan, Natasha, et al. "Role of 19 SNPs in 10 genes with type 2 diabetes in the Pakistani population." Gene 848 (2023): 146899.

Abdollahi, Jafar, and Babak Nouri-Moghaddam. "Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction." Iran Journal of Computer Science 5.3 (2022): 205-220.

Florez, Jose C., and Ewan R. Pearson. "A roadmap to achieve pharmacological precision medicine in diabetes." Diabetologia 65.11 (2022): 1830-1838.

Deutsch, Aaron J., Emma Ahlqvist, and Miriam S. Udler. "Phenotypic and genetic classification of diabetes." Diabetologia 65.11 (2022): 1758-1769.

Srinivasa Sai Abhijit Challapalli. (2024). Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic. Journal of Sensors, IoT & Health Sciences, 2(2024): 40-55.

Whitfield, John B., et al. "A genetic risk score and diabetes predict the development of alcohol-related cirrhosis in drinkers." Journal of Hepatology 76.2 (2022): 275-282.

Yang, Hui, et al. "Risk prediction of diabetes: big data mining with the fusion of multifarious physical examination indicators." Information Fusion 75 (2021): 140-149.

Naz, Huma, and Sachin Ahuja. "Deep learning approach for diabetes prediction using PIMA Indian dataset." Journal of Diabetes & Metabolic Disorders 19 (2020): 391-403.

Tigga, Neha Prerna, and Shruti Garg. "Prediction of type 2 diabetes using machine learning classification methods." Procedia Computer Science 167 (2020): 706-716.

Srinivasa Sai Abhijit Challapalli. Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments. Journal of Sensors, IoT & Health Sciences, 2(2024), 1-15.

Zhou, Huaping, Raushan Myrzashova, and Rui Zheng. "Diabetes prediction model based on an enhanced deep neural network." EURASIP Journal on Wireless Communications and Networking 2020 (2020): 1-13.

K. Vijay Kumar, S. Sravanthi, Syed Shujauddin Sameer, & K. Anil Kumar. Effective Data Aggregation Model for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment. Journal of Sensors, IoT & Health Sciences, 1(2023): 40-50.

Swapna Saturi, Rafia Adiba, I. Yeshwanth Reddy, Md.Arsalan Shareef, & D.Prathyush. Advanced MRI-Based Alzheimer ’s disease Classification with Hybrid Convolutional Neural Networks. Journal of Computer Allied Intelligence, 3(2025): 31-39.

Rai, Vivek, et al. "Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures." Molecular Metabolism 32 (2020): 109-121.

Redondo, Maria J., et al. "The clinical consequences of heterogeneity within and between different diabetes types." Diabetologia 63 (2020): 2040-2048.

Alex, Suja A., et al. "Deep LSTM Model for Diabetes Prediction with Class Balancing by SMOTE." Electronics 11.17 (2022): 2737.

Ellahham, Samer. "Artificial intelligence: the future for diabetes care." The American journal of medicine 133.8 (2020): 895-900.

Park, Byoungjin, Hye Sun Lee, and Yong-Jae Lee. "Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort." Translational Research 228 (2021): 42-51.

Awotunde, Joseph Bamidele, et al. "Prediction and classification of diabetes mellitus using genomic data." Intelligent IoT systems in personalized health care. Academic Press, 2021. 235-292.

Abdallah, Maher, et al. "Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University." Diabetology & Metabolic Syndrome 12 (2020): 1-11.

Chatrati, Saiteja Prasad, et al. "Smart home health monitoring system for predicting type 2 diabetes and hypertension." Journal of King Saud University-Computer and Information Sciences 34.3 (2022): 862-870.

Laakso, Markku. "Biomarkers for type 2 diabetes." Molecular Metabolism 27 (2019): S139-S146.

Jin, Heejin, et al. "Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study meta-analysis." BMC Medicine 21.1 (2023): 16.

Lu H, Uddin S, Hajati F, Moni MA, Khushi M. A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. Applied Intelligence. 2022 Feb;52(3):2411-22.

Ganie SM, Malik MB. An ensemble machine learning approach for predicting type-II diabetes mellitus based on lifestyle indicators. Healthcare Analytics. 2022 Nov 1;2:100092.

Shuja M, Mittal S, Zaman M. Effective prediction of type II diabetes mellitus using data mining classifiers and SMOTE. Advances in Computing and Intelligent Systems: Proceedings of ICACM 2019 2020 (pp. 195-211). Springer Singapore.

Balasubramanian S, Kashyap R, CVN ST, Anuradha M. Hybrid prediction model for type-2 diabetes with class imbalance. 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) 2020 Dec 20 (pp. 1-6). IEEE.