Analysis of Geographical Characteristics and Risk Factors for Non-Communicable Diseases: Diabetes in Central Java Using Random Forest and SHAP

Diabetes mellitus is a growing health issue in Indonesia, particularly in Central Java Province.In 2023, it contributed significantly to public health concerns due to its increasing prevalence.This study utilizes the Random Forest algorithm to identify dominant diabetes risk factors and map their geographic distribution across the region.The analysis incorporates community lifestyle indicators such as the number of sweet drink stores, gyms, sports halls, transportation availability, karaoke venues, tourist attractions, fast food outlets, and alcohol stores in each regency/city.Results indicate that the number of tourist attractions (feature importance: 0.2817) and sweet drink stores (feature importance: 0.1502) are the primary global risk factors.Locally, tourism dominates as the key risk factor in 18 regencies/cities (51.4% of the region).The model employs optimal hyperparameter tuning to improve accuracy and utilizes SHAP techniques to evaluate the importance of local features.This study aims to enhance understanding of the key risk factors contributing to diabetes cases and their distribution in Central Java Province.

Authors

Institutions

Publication Details

Journal
Proceedings of International Conference on Artificial Life and Robotics
Published
2025-02-13
DOI
https://doi.org/10.5954/icarob.2025.os26-6
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Geographical Characteristics and Risk Factors for Non-Communicable Diseases: Diabetes in Central Java Using Random Forest and SHAP

Andi Prademon Yunus, Ambar Arum Prameswari
1 links
Proceedings of International Conference on Artificial Life and Robotics
Data Mining and Machine Learning Applications
article

Analysis of Geographical Characteristics and Risk Factors for Non-Communicable Diseases: Diabetes in Central Java Using Random Forest and SHAP

Andi Prademon Yunus, Ambar Arum Prameswari
article en

Abstract

Diabetes mellitus is a growing health issue in Indonesia, particularly in Central Java Province.In 2023, it contributed significantly to public health concerns due to its increasing prevalence.This study utilizes the Random Forest algorithm to identify dominant diabetes risk factors and map their geographic distribution across the region.The analysis incorporates community lifestyle indicators such as the number of sweet drink stores, gyms, sports halls, transportation availability, karaoke venues, tourist attractions, fast food outlets, and alcohol stores in each regency/city.Results indicate that the number of tourist attractions (feature importance: 0.2817) and sweet drink stores (feature importance: 0.1502) are the primary global risk factors.Locally, tourism dominates as the key risk factor in 18 regencies/cities (51.4% of the region).The model employs optimal hyperparameter tuning to improve accuracy and utilizes SHAP techniques to evaluate the importance of local features.This study aims to enhance understanding of the key risk factors contributing to diabetes cases and their distribution in Central Java Province.

Proceedings of International Conference on Artificial Life and RoboticsVol. 30
Telkom University (ID)
Universitas Telkom
Zero hunger
Openalex Percentile: Top 66%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.