Enhanced Machine Learning-based Diagnostic Model for Lassa Fever

Abstract

The research presents a machine learning-based model for the diagnosis of Lassa fever. The development of the ML Model involves the collection of the Lassa fever dataset from the Infectious Diseases Control Centre of the Federal Medical Centre, Owo Ondo state, it was preprocessed and Five machine learning algorithms namely Naïve Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Decision Tree were trained and tested using 10-fold cross-validation technique and evaluated using standard metrics. Four of the classifiers had an accuracy of 90% and above while k-Nearest Neighbour had the lowest accuracy of 87.7%. Naïve Bayes and Support Vector Machine performed well with an accuracy of 93.2% and Precision of 97.0%. The AUC measure for the NB and SVM algorithm was calculated to be 92.9 %. The Diagnostic Accuracy (DA) of the Existing Method (EM) calculated using standard metrics and evaluated through simple statistical techniques was 61.1%. The Machine Learning (ML) models performed better and more effectively in aiding the diagnosis of Lassa fever cases than the EM as determined from the dataset. The recommended ML model also has the potential to improve Medical diagnosis and eliminate the cumbersome, time-consuming and costly nature of manual monitoring of Lassa suspects in the current practice and can serve as a basis for further investigation/implementation of ML applications in the diagnosis of other similar diseases.