Article Per Year (5 Year)
p-Index From 2021 - 2026
1.756
P-Index
This Author published in this journals
All Journal International Journal of Public Health Science (IJPHS) Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Ners ASEAN Journal of Community Engagement J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan Jurnal Kesehatan Masyarakat
Rico Kurniawan
Universitas Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Harnessing Sociodemographic and Anthropometric Insights to Predict Type 2 Diabetes Risk: A Machine Learning Approach Kurniawan, Rico; Yunanda, Rezki; Assetya Pratiwi, Okky
Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan Vol. 5, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The complexity of diabetes makes early diagnosis and effective management challenging for healthcare settings. This study developed and evaluated models using machine learning algorithms to predict the risk of diabetes. The data were primarily sourced from the Indonesia Family Life Survey (IFLS). Diabetes status was identified using A1c whole blood sample values (A1c WBS ³5.7%). Sociodemographic and anthropometric factors were set as predictors, most of which were significantly correlated with diabetes status. Discrete machine learning algorithms such as decision tree, k-nearest neighbors (KNN), random forest (RF), naïve Bayer, support vector machine (SVM), and neural network (MLP) were applied to construct and compare prediction models for classification. MLP model exhibited the highest performance with AUC 67%, Accuracy 87.1%, F1 Score 87.1%, Recall 87.1% and Precision 82.4%. Overall, machine learning models were found highly viable in predicting disease outcomes with increasing accuracy. Their usage would allow healthcare professionals to make more informed patient care decisions. Future research initiatives should attempt to further enhance these models.
Co-Authors ., Dadun Al-Muqhni, Muhammad Kyrgizt Alwin, Wanarani Andayani, Gitalisa Apriliantika, Wayan Wahyu Artha Prabawa Bahar, Ryza Jazid Bani, Anna Puspitasari Bun Yan Khiong Daniel, Hisar Diaini, Meindy Diastri, Anggraina Dwiranti, Astari Fadhilah, Hafsah Farah Fadhilah, Hafshah Farah Febrina Rahmayanti Fitriana, Anis Fitriyani, Lia Harini, Melinda Ida Ruwaida, Ida Indang Trihandini Irawati, Yunia Jayawarsa, A.A. Ketut Kario, Asrit Jessica Lily Thamrin Lina Widyastuti Luh Karunia Wahyuni, Luh Karunia Luzanil, Sonny Tirta Made Susiyanti Matsurah, Qaishum Menaldi, Adhityawarman Menaldi, Sri Linuwih SW Muhammad Kodir Neng Tine Kartinah Almuktabar Nuridzin, Dion Zein Okky Assetya Pratiwi Priscilia, Florentina Ryza Jazid Baharuddin Nur Sahid, Muhammad Hidayat Sayekti Yuliyanti Siti Fathonah Sudibyo Alimoeso Tri Rahayu Welcy Fine Wicaksono, Gunawan Yeni Dwi Lestari Yunanda, Rezki