Enhancing Liver Disease Detection and Management with Advanced Machine Learning Models

Koduganti Venkata Rao; Juthuka Aruna Devi; Yarlagadda Anuradha; Kumari G; Mirtipati Satish Kumar; M. Somasundara Rao

doi:10.52756/ijerr.2024.v42.009

Authors

Koduganti Venkata Rao Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India https://orcid.org/0000-0002-1633-7236
Juthuka Aruna Devi Department of Computer Science and Engineering (AI&ML,DS), Anil Neerukonda Institute of Technology and Sciences (A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0004-0003-7257
Yarlagadda Anuradha Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0000-4863-4027
Kumari G Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0001-1230-0989
Mirtipati Satish Kumar Department of Computer Science and Engineering, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India https://orcid.org/0009-0000-2920-7812
M. Somasundara Rao Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram, Andhra Pradesh, India https://orcid.org/0000-0002-2998-2533

DOI:

https://doi.org/10.52756/ijerr.2024.v42.009

Keywords:

ALB, ALP, ALT, AST, BIL, Decision trees, Ensemble Learning, Random Forest Classifier

Abstract

The prevalence of hearing loss has risen making it a significant public health issue. Hearing loss is caused by complicated pathophysiological pathways, with various risk factors identified, such as hereditary factors, inflammatory processes, systemic disorders, noise exposure, medicines, oxidative stress, and age. Metabolic syndrome is a medical condition characterized by the presence of hypertension, central obesity, hyperlipidemia, and diabetes. Metabolic syndrome has been linked to several clinical diseases, such as stroke, heart attack, cardiovascular disease-related death, and diabetes. A cross-sectional study was done on 100 patients with metabolic syndrome which used specific cut-off points of waist circumference, fasting glucose levels, blood pressure, triglyceride, and high-density lipoprotein cholesterol levels to diagnose the condition. Among these five criteria, at least three had to be met, and the presence of additional criteria indicated greater severity. Audiological evaluation with pure tone audiometry was done and recorded. Statistical analysis was performed to determine the significance of the results. The majority of the patients (62%) had unilateral hearing loss, amongst which sensory-neural type and moderately severe hearing loss were the most common type (67%) and severity (61%) of hearing loss respectively. Chi-square tests were done for the comparison of type, severity, and laterality of hearing loss with age, gender of the patients, and criteria fulfilled for metabolic syndrome. The severity of hearing loss had a statistically significant association with the age of the patients and the number of criteria fulfilled for metabolic syndrome with a p-value of 0.003. There was a statistically significant association between the severity of hearing loss and the age of the patients and the number of criteria fulfilled for metabolic syndrome with a p-value of 0.004. Metabolic syndrome affects the auditory system in several ways. It damages hearing and exacerbates presbycusis. Hearing loss worsens as components of the metabolic syndrome increase.

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