Raspberry Pi-Based Medical Expert System for Pre-Diagnosis of Mosquito-Borne Diseases

This paper presents a Raspberry Pi-based medical expert system that provides a preliminary diagnosis for patients who could be suffering from the mosquito-borne diseases: dengue, chikungunya, and malaria. The Philippines, being a tropical country, is endemic to the said diseases. The continuously declining health services and the high disparity between the number of doctors and patients in the Philippines motivated the proponents of the study to develop such system. The expert system is created by incorporating fuzzy logic with weighted rules. The system, comprising of a human-machine interface, temperature sensor, and electronic blood pressure monitor with built-in pulse rate sensor, records user's vital signs and symptoms resembling a conventional check-up routine. By comparing the system's preliminarily diagnosis with the expert's diagnosis in a total of 80 tests with 20 tests per disease, 71.67%, 83.33% and 91.67% of the time, the system correctly pre-diagnosed dengue, chikungunya, and malaria respectively. For diseases that are not dengue, malaria, nor chikungunya, the system correctly identified the unlikelihood of having the said mosquito-borne diseases 91.67% of the time. Moreover, a chi-square test is also conducted; with a level of significance of 0.05 yielded a p-value of 0.464. Results show that the system can assess the patient to provide pre-diagnosis for the likelihood of dengue, chikungunya, and malaria. This is beneficial in the case of the inadequate proper medical attention given to patients in public hospitals-especially in rural areas.