A Graphical User Interface Based on Logistic Regression Approach for Malarial Detection

Abstract

Malaria (a mosquito-infected disease) is one of the deadliest communicable diseases in the world. The disease causes a significant global health challenge. According to the World Health Organisation (WHO), millions of deaths occur every year worldwide. The mortality rate poses a challenge to authority and management. Over the years, mathematical and machine learning (ML)-based techniques have been developed to mitigate the scenario. In this study, ML-based prediction techniques are investigated to predict the presence of malaria in individuals. More specifically, three ML-based techniques—Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)—are employed to differentiate their prediction performance (namely, classification accuracy, precision, recall, and F-score) over a created database (D) consisting of 350 records. Among the adopted techniques, the LR technique shows overall better performance over the test data chosen from D. A graphical user interface (GUI) based on LR is also developed to detect the presence or absence of malaria in any individual.