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ABSTRACT
Although significant progress towards elimination of malaria has been made, it still remains a major health issue in many tropical regions where it thrives in countries with a weak healthcare system. Significant increases in investment have resulted in the development of new tools to combat this parasitic disease. Some of the newest tools require expensive and complex technologies that are not available to national malaria programmers. However, in order to control malaria (reduction of morbidity and mortality) and eliminate it (interruption of the transmission cycle), it is essential to identify and treat infected individuals early in the course of the illness. To achieve this goal, everyone living in malaria-endemic areas must have easy access to reliable diagnostics and effective treatment. In this study, an Artificial Neural Network classifier algorithm was developed for the early diagnosis of malaria. The program was developed with Anaconda Spyder 3.7. Test run shows that the program can effectively predict malaria as part of the said early diagnosis.
