Cited 96 times in

Machine learning model for predicting malaria using clinical information

Authors
 You Won Lee  ;  Jae Woo Choi  ;  Eun-Hee Shin 
Citation
 COMPUTERS IN BIOLOGY AND MEDICINE, Vol.129 : 104151, 2021-02 
Journal Title
COMPUTERS IN BIOLOGY AND MEDICINE
ISSN
 0010-4825 
Issue Date
2021-02
MeSH
Humans ; Machine Learning* ; Malaria* / diagnosis ; Neural Networks, Computer ; Support Vector Machine
Keywords
Case reports ; Diagnosis ; Machine learning ; Malaria ; Patient information
Abstract
Background: Rapid diagnosing is crucial for controlling malaria. Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. This study developed a machine learning model for malaria diagnosis using patient information.

Methods: To construct datasets, we extracted patient information from the PubMed abstracts from 1956 to 2019. We used two datasets: a solely parasitic disease dataset and total dataset by adding information about other diseases. We compared six machine learning models: support vector machine, random forest (RF), multilayered perceptron, AdaBoost, gradient boosting (GB), and CatBoost. In addition, a synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem.

Results: Concerning the solely parasitic disease dataset, RF was found to be the best model regardless of using SMOTE. Concerning the total dataset, GB was found to be the best. However, after applying SMOTE, RF performed the best. Considering the imbalanced data, nationality was found to be the most important feature in malaria prediction. In case of the balanced data with SMOTE, the most important feature was symptom.

Conclusions: The results demonstrated that machine learning techniques can be successfully applied to predict malaria using patient information.
Full Text
https://www.sciencedirect.com/science/article/pii/S0010482520304820
DOI
10.1016/j.compbiomed.2020.104151
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/192412
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links