24.05.148
003.3 - Computer science- system- computer modeling and simulation
Karya Ilmiah - Thesis (S2) - Reference
Data Science
28 kali
<p><em>Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the International Diabetes Federation's alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. </em><em>This study focuses on predicting the Length of Stay (LOS) of Diabetes Mellitus patients using three machine learning methods: Logistic Regression, Random Forest, and XGBoost</em><em>. Concurrently, Process Mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the location of healthcare facility, patient age, and arrival time, are unveiled. </em><em>Random Forest achieves an accuracy score</em><em> 0.88</em><em> with an F1 score of 0.82, Precision of 0.82, and Recall of 0.81, with a Time Prediction of 0.1027 seconds. This accuracy is higher compared to Logistic Regression </em><em>with accuracy 0.76, F1 score 0.63 , Pecision0.57, Recall 0.6 and time prediction 0.00062. While XGBoost gets an accuracy of 0.86, F1 score 0.79, Precision 0.79, Recall 0.79 and time prediction 0.06499</em><em>.</em> <em>Certain medical procedures, such as those involving 'Diabetes & Nutritional/Metabolic Disorders,' represent treatments with the longest sojourn time and the highest frequency of cases across all LOS categories</em><em>. Giving procedures multiple times can affect the patient's LOS, especially for procedure Inpatient with </em><em>Peripheral Vascular Disorders with mild, moderate, and severe level procedures and Inpatient Care in the General Ward</em><em>. </em><em>F</em><em>acility usage like is Puskesmas being the primary destination for diabetes patients</em><em> and </em><em>ha</em><em>s</em><em> longer lengths of stay compared to general practitioners</em><em> (Dokter Umum)</em><em> or primary clinics</em><em> (Klinik Pratama)</em><em>. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Random Forests get superior performance, than Logistic Regression and XGBoost. </em></p>
<p>Keywords: Machine Learning, Process Mining, Length of Stay, Logistic Regression,Random Forest XGBoost, BPJS</p>
Tersedia 1 dari total 1 Koleksi
Nama | FITRI EKA CAHYANTI |
Jenis | Perorangan |
Penyunting | Adiwijaya, Angelina Prima Kurniati |
Penerjemah |
Nama | Universitas Telkom, S2 Informatika |
Kota | Bandung |
Tahun | 2024 |
Harga sewa | IDR 0,00 |
Denda harian | IDR 0,00 |
Jenis | Non-Sirkulasi |