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  • E-ISSN 3022-5388

Development of Medical Cost Prediction Model Based on the Machine Learning Algorithm

Journal of Korean Artificial Intelligence Association / Journal of Korean Artificial Intelligence Association, (E)3022-5388
2023, v.1 no.1, pp.11-16
https://doi.org/10.24225/jkaia.2023.1.1.11
Han Bi KIM (Eulji University)
Dong Hoon HAN (Medical Artificial Information Center)
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Abstract

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

keywords
Machine learning, Algorithm, Models, Regression, Prediction, Azure Mchine Learning Studio, R, Error, Accuracy, Performance metrics, Health care


Submission Date
2023-12-21
Revised Date
2023-12-21
Accepted Date
2023-12-30
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Journal of Korean Artificial Intelligence Association