Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models

Abstract

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi’s orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole.

Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors

“…By coordinating atomic information with machine learning models, they pointed to reveal novel biomarkers and pathways related to cardiovascular illness pathophysiology, subsequently encouraging personalized hazard evaluation and treatment strategies. Rankovic et al (2023) [15] enhanced hyperinsulinemia diagnostics by utilizing ANN-L(atin square) models. Their investigation centred on moving forward the precision and productivity of hyperinsulinemia conclusion utilizing fake neural systems and Latin square test plans, advertising a novel approach to affront resistance appraisal and administration.…”

Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

“…Moreover, AI can be instrumental in identifying gaps in healthcare provision and hidden risk categories. By scrutinizing patient data, ML algorithms can identify high-risk individuals who might be overlooked by traditional risk assessment tools [14] . This information can be used to formulate improved care plans, ensuring that high-risk patients receive adequate attention to effectively manage their conditions [15] , [16] .…”

“…In conclusion, the integration of AI in healthcare holds immense potential to revolutionize the industry by enhancing the quality of healthcare services, improving the treatment outcomes for complex diseases, and revealing previously unidentified gaps in healthcare provision and hidden risk categories. As these technologies advance and evolve, it is expected that innovative applications of AI in healthcare will continue to emerge in the forthcoming years [14] , [15] , [16] .…”