Obtaining Elderly Patients’ Lifestyle Information from Unstructured Text Sources

Defry Hamdana (Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Japan)
Asmaul Husna (Informatics Engineering Departement, Engineering Faculty, Malikussaleh University, Indonesia)

Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Vol. 3 (2022): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI

In this work, we made many simulations to take information from free-text notes belonging to the patient that indicates his or her habits. Detailed information about a patient's habits will allow the nurse to create a personalized daily schedule. Due to the fact that each patient has a different routine. The information is separated into five categories: dietary habits, drinking/smoking habits, excretion habits/toilet style, fashionable/colour preference/footwear, and favourite music/radio/TV shows. To realize this, we use six machine learning models, there are Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Multinomial Logistic Regression (MLR), and Gradient Boosting (GB). As a result, all models have more than 90% accuracy on both train sets and test sets.In this study, we focus on SVM with Train Set Accuracy score 93.9% and Test Set Accuracy score 94.1%, which is more resistant to overfitting issues.