MODEL PREDIKSI RISIKO HIPERTENSI PADA DEWASA DENGAN OVERWEIGHT MENGGUNAKAN SUPERVISED MACHINE LEARNING NAÏVE BAYES

INAYAH, TSABITAH RAMADHANI and Liberty, Iche Andriyani and Novita, Emma (2024) MODEL PREDIKSI RISIKO HIPERTENSI PADA DEWASA DENGAN OVERWEIGHT MENGGUNAKAN SUPERVISED MACHINE LEARNING NAÏVE BAYES. Undergraduate thesis, Sriwijaya University.

	Text RAMA_11201_04011382126209.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (12MB) | Request a copy
	Text RAMA_11201_04011382126209_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (3MB)
	Text RAMA_11201_04011382126209_0007029001_0003118105_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (895kB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (331kB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (87kB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (87kB) | Request a copy
	Text RAMA_11201_04011382126209_0007029001_0003118105_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy

Abstract

Hypertension is a global health issue often associated with overweight, a condition characterized by excessive fat accumulation that can increase the risk of hypertension by 3-4 times. Machine Learning approaches, such as the Naïve Bayes algorithm, have proven effective in analyzing complex data and predicting various health conditions, including hypertension in individuals with overweight. This study utilized a cross-sectional design applying the Supervised Machine Learning Naïve Bayes algorithm to predict the risk of hypertension in overweight adults. Data were collected from adult patients aged 19–65 years visiting a primary health center in Palembang City in July 2024. Variables analyzed included age, gender, family history, education, income, occupation, central obesity, physical activity, and dietary patterns (salty food, vegetables, fruit). Model performance was evaluated using a confusion matrix, accuracy, precision, sensitivity, and F1-score. The Naïve Bayes prediction model demonstrated high performance in predicting hypertension in overweight individuals, achieving an accuracy of 95.1%. Key factors influencing the prediction of hypertension conditions in overweight individuals included age, gender, physical activity, and central obesity. The Naïve Bayes algorithm is effective in predicting the risk of hypertension in overweight adults, offering opportunities to implement more targeted preventive interventions. This model is expected to assist healthcare practitioners in reducing hypertension prevalence and improving community quality of life.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hypertension, Overweight, Naïve Bayes, Machine Learning, Prediction Model Hypertension with Overweight.
Subjects:	R Medicine > R Medicine (General) > R5-920 Medicine (General)
Divisions:	04-Faculty of Medicine > 11201-Medicine (S1)
Depositing User:	Tsabitah Ramadhani Inayah
Date Deposited:	19 Dec 2024 02:38
Last Modified:	19 Dec 2024 02:38
URI:	http://repository.unsri.ac.id/id/eprint/160783

Actions (login required)

View Item