Abstract
The development of personal health recommendation systems has become a significant focus of research, along with the increasing need to improve user well-being. This research critically examines options in data and data mining and Machine Learning to support the establishment of systems to recommend better and improved healthcare services. Drawing on the results of a systematic review of past literature, the study shows that the application of Machine Learning algorithms in disease opinion learning, health condition classification, and demographics is emerging as a prospect for driving clinical decisions. Various models, from Deep Learning to traditional machine learning models such as Support Vector Classifier and K-Neighbor Value, have been used to assess individual data. On the other hand, the use of IoT in remote diagnosis and AI-based health applications has also shown that it not only improves diagnosis accuracy but also makes users active in their health management experience. In this study, we try to identify the key components of an efficient personalized health recommendation system and discuss the challenges and opportunities related to its implementation. Therefore, we can ensure that the resulting SRS brings better solutions for early detection, personalized health interventions, and overall user well-being.
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Recommended Citation
Sampetoding, Eliyah Acantha Manapa; Faiqihah, Andi Alisha; and Pongtambing, Yulita Sirinti
(2025)
"Development of a Personal Health Recommendation System Based on Data and Machine Learning to Improve User Wellbeing,"
Jurnal Administrasi Bisnis Terapan: Vol. 7:
Iss.
1, Article 7.
Available at:
https://scholarhub.ui.ac.id/jabt/vol7/iss1/7
