Abstract: Numerous studies have been carried out on computation and its applications to medical data with proven benefits for improving the quality of public health. However, not all research results or practical applications can be applied to all conditions but must be in accordance with the various contexts such as community culture, geographical, or citizen behaviors. Unfortunately, the use of digital data in Indonesia is still very limited. The study objective is to assess various data mining techniques to utilize data from laboratory test results collected from a private hospital in Indonesia in predicting the next patient treatment. Furthermore, various machine learning classification techniques were explored for the purpose. Based on the experiments, it was concluded that XGBoost with hyperparameter tuning produced the best accuracy level at 0.7579, compared to other classifiers. A better level of accuracy can be obtained by enriching the type of dataset used, such as the patient's medical record history.

Mujiono Sadikin, Ida Nurhaida and Ria Puspita Sari, “Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120248

@article{Sadikin2021,
title = {Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120248},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120248},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Mujiono Sadikin and Ida Nurhaida and Ria Puspita Sari}
}