Machine Learning Classifiers for Symptom-Based Malaria Prediction

Abstract

A patient's previous history of malaria plays an important role in malaria prediction based on symptoms. Malaria patients who have previously suffered from malaria are highly likely to develop different symptoms from those without a previous history of malaria. To predict the malaria presence for both patients with and without a previous history of malaria, we build two separate malaria classifiers based on two different sets of symptoms using four machine learning techniques including neural networks (NNs), logistic regression (LR), support vector machines (SVMs) and k-nearest neighbors. These malaria classifiers are built using medical records collected from patients suffering from malaria and other febrile diseases. Extensive experiments conducted show that the two NN classifiers slightly outperform the other classifiers. The NN classifier for patients with a previous history of malaria achieves excellent performance for accuracy, recall and F1-score with 95.76%, 95.41% and 95.76% respectively. The LR classifier outperforms the other three classifiers for precision with 97.14%. The NN classifier for patients without a previous history of malaria also achieves superior performance in accuracy and precision with 88.48% and 87.34% respectively. The SVM classifier outperforms other classifiers in terms of recall and F1-score with 94.62% and 88.04% respectively. With high recall rates, the four classifiers are suitable for symptom-based malaria prediction. These results justify the development of two separate malaria classifiers and provide valuable insights into building symptom-based malaria classifiers based on patients' previous history of malaria.