Comparison of Machine Learning Algorithms for Classification of Drug Groups

Sisfotenika

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Field Value
 
Title Comparison of Machine Learning Algorithms for Classification of Drug Groups
 
Creator Purwono, Purwono
Wirasto, Anggit; Universitas Harapan Bangsa
Nisa, Khoirun; Universitas Harapan Bangsa
 
Subject Classification; Machine; Learning; Healthcare; Drugs;
 
Description The stages of clinical trials need to be carried out when determining a new drug group for patient management. This stage is considered quite long and requires a lot of money. Medical record system data continues to grow all the time. The data can be analyzed to find a pattern of grouping drugs used in the treatment of patients based on their body condition. Utilization of artificial intelligence (AI) technology can be done to classify drug data used during patient care. Machine learning as a branch of science in the AI field can be a solution to deal with these problems. Machines will learn, analyze, and predict drug requirements quickly with less cost. Based on related research, we contribute to comparing the performance of the best machine learning algorithms that can be used as drug classification models. The results of this study are the accuracy of the support vector machine algorithm is 94.7% while the random forest and decission tree algorithms are 98.2%. This shows that the algorithms that can be considered as a drug classification model are random forest and decision tree. This model needs to be tested on a larger dataset to produce the best accuracy value.
 
Publisher STMIK PONTIANAK
 
Contributor
 
Date 2021-07-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1134
10.30700/jst.v11i2.1134
 
Source SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 196-207
SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 196-207
2460-5344
2087-7897
10.30700/jst.v11i2
 
Language ind
 
Relation http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1134/766
 
Rights Copyright (c) 2021 SISFOTENIKA
http://creativecommons.org/licenses/by/4.0
 

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