Prediction of measles patients using machine learning classifiers: a comparative study

Abstract

Background Measles has high primary reproductive number, extremely infectious and ranked second to malaria in terms of disease burden in Ghana. Owing to the disease’s high infectious rate, making early diagnosis based on an accurate system can help limit the spread of the disease. Studies have been conducted to derive models to serve as preliminary tools for early detection. However, these derived models are based on traditional methods, which may be limited in terms of model sensitivity and prediction power. This study focuses on comparing the performance of five machine learning classification techniques with a traditional method for predicting measles patients in Ghana. The study was an analytical cross-sectional design of suspected measles cases in Ghana. Results The performance of six classifiers were compared and the random forest (RF) model demonstrated better performance among other models. The RF model achieved the highest sensitivity (0.88) specificity (0.96), ROC (0.92) and total accuracy (0.92). Conclusions Our findings showed that, despite all the six methods had good performance in classifying measles patients, the RF model outperformed all the other classifiers in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting measles in the early stage.

Applied machine learning techniques for chronic disease treatment default prediction and its potential benefits for patient outcome: A case series study approach

“…Comparative study to predict measles with 1,797 suspected cases used six machine learning techniques to determine 78 positive cases for measles and 1,696 were identified as negative cases creating a class distribution imbalance. Results obtained showed superior random forest classifier performance in specificity 96%, sensitivity 88%, receiver operating characteristic curve score 92% and total prediction accuracy score 92% [16] than the other modeling techniques (generalized linear model, decision tree, naïve bayes, support vector machines, artificial neural network). Applied learning technique assessment study for the detection and management of infectious diseases caused by fatal or life-threatening causative agents capable of infecting both animals and humans provided a comprehensive review of machine learning application use in pathogen detection, public health surveillance, host-parasite interaction, drug discovery, omics and vaccine discovery.…”