Machine learning model for predicting malaria using clinical 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.