Development of an Antimalarial Recommender System to Accommodate Patient-specific Factors using an XGBoost Algorithm

The rate of malaria in Nigeria is alarmingly high however, what is more alarming is the quality of treatment of the most common disease in the country. Nigeria has the highest malaria morbidity rates in the world[1]. This study aimed at developing a chronic disease epidemiology risk system for recommending antimalarials in Nigeria based on patient-specific factors. This study explores the development of an antimalarial recommender system using an XGBoost algorithm. This algorithm was trained on a synthesized dataset, with a size of 516 rows, derived from the NAFDAC dataset containing the list of all endorsed antimalarials in Nigeria as well as the integration of the prescription guidelines given by WHO.Some of the factors considered when prescribing the drugs were weight, pregnancy status, malaria severity, and other medical condition. The chosen algorithm, XGBoost algorithm, was benchmarked against SVM, random forest, deep neural, and was selected due to its accuracy. From the XGBoost model, the accuracy result was 0.9811 and based on the System Usability Scale, the system scored a 83.15% highlighting how user-friendly the interface is. Overall, this antimalarial recommender system aids the quality of treatment of malaria.