MACHINE LEARNING MODEL ARTIFICIAL NEURAL NETWORK UNTUK PREDIKSI RISIKO OBESITAS DAN PREDIABETES PADA DEWASA

SUGIHARTO, VIVI and Liberty, Iche Andriyani and Roflin, Eddy (2024) MACHINE LEARNING MODEL ARTIFICIAL NEURAL NETWORK UNTUK PREDIKSI RISIKO OBESITAS DAN PREDIABETES PADA DEWASA. Undergraduate thesis, Sriwijaya University.

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Abstract

Background: Obesity is a clinical condition characterized by abnormal fat accumulation and has significantly increased in prevalence since 1980. Obesity and prediabetes are complexly related to factors such as age, gender, and lifestyle. Obesity contributes to insulin resistance, dyslipidemia, and beta-cell dysfunction, which progresses to type 2 diabetes mellitus. These conditions may occur concomitantly. Predictive analysis of obesity and prediabetes often involves complex statistical processes, making machine learning, particularly Artificial Neural Networks (ANN), a valuable tool to enhance predictive accuracy. This study aims to evaluate the accuracy of the ANN model in predicting obesity and prediabetes in adult populations. Methods: This observational analytic study used a cross-sectional design and secondary data. The sample consisted of adults aged 19–65 years living in Palembang, who visited primary healthcare services. Data were collected using purposive sampling and analyzed with multinomial logistic regression and ANN model from Orange Data Mining software. Results: Of the 1212 respondents, 63 (5.2%) had both obesity and prediabetes, 182 (15%) were obese, 125 (10.3%) had prediabetes, and 842 (69.5%) were normal. The ANN model demonstrated an accuracy of 91.7% in predicting obesity and prediabetes, 89.2% for obesity, and 81.3% for prediabetes. Key predictors included age, gender, family history of obesity, income level, consumption of sugary foods, fruits, and physical activity. Conclusion: Artificial Neural Network is accurate in predicting obesity and or prediabetes in adults. Keywords: Obesity, Prediabetes, Insulin Resistance, Concomitant, Machine Learning, Artificial Neural Network

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Obesity, Prediabetes, Insulin Resistance, Concomitant, Machine Learning, Artificial Neural Network
Subjects:	R Medicine > R Medicine (General) > R5-920 Medicine (General)
Divisions:	04-Faculty of Medicine > 11201-Medicine (S1)
Depositing User:	Vivi Sugiharto
Date Deposited:	20 Dec 2024 03:29
Last Modified:	20 Dec 2024 03:29
URI:	http://repository.unsri.ac.id/id/eprint/160928

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