Integrating machine learning in electronic health passport based on WHO study and healthcare resources
Ismukhamedova A. Uvaliyeva I. Belginova S.
January 2024Elsevier Ltd
Informatics in Medicine Unlocked
2024#44
Healthcares evolution amid rapid scientific progress demands the efficient management of extensive data. This has given rise to electronic health passports, which play a crucial role in modern healthcare by providing secure access to comprehensive medical histories. This study, exemplified by diabetes, addresses the mounting global prevalence of this disease, which saw cases soar from 108 million in 1980 to 422 million by 2014, resulting in a 5 % rise in diabetes-related premature mortality from 2000 to 2016. Disease prediction based on patient attributes holds potential to optimize healthcare resource allocation. The research aim is to identify new hidden dependencies and parameters in the diagnosis of diabetes, utilizing machine learning techniques. The first stage of this involves a thorough comparison and optimization of various modeling techniques, from Decision Trees (e.g., Random Forest, Adaboost) to advanced Deep Learning models like CNN, RNN, and LSTM networks, aiming to determine the most effective implementation method. Leveraging a subset of the MIMIC-III Critical Care Database, a comprehensive de-identified health-related dataset, this research provides insights into addressing the escalating diabetes challenge. The study revealed that the Gradient Boosting Machine was the most effective for making diagnoses. In the realm of deep learning, the Recurrent Neural Network (RNN) demonstrated the best results. This work aligns with the broader context of utilizing electronic health passports to enhance healthcare efficiency and decision-making in an era characterized by abundant health data. It is essential to emphasize that this work represents the initial phase of a broader research trajectory, and its continuation will be expounded upon in forthcoming publications. Dataset: https://doi.org/10.13026/C2XW26. Dataset license: PhysioNet Credentialed Health Data Use Agreement 1.5.0.
Data preprocessing , Deep learning , Diabetes prevalence , Healthcare resource allocation , Machine learning , MIMIC-III critical care database , Modeling techniques , Patient attributes , WHO study
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School of computer Science, D. Serikbayev East Kazakhstan Technical University, East Kazakhstan, Ust-Kamenogorsk, 070004, Kazakhstan
School of Digital Technology and Intelligent Systems, D. Serikbayev East Kazakhstan Technical University, East Kazakhstan, Ust-Kamenogorsk, 070004, Kazakhstan
Department of Information Technology, University Turan, Almaty, 050005, Kazakhstan
School of computer Science
School of Digital Technology and Intelligent Systems
Department of Information Technology
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