Over a million people across the globe die annually due to mosquito-borne diseases. As a tropical country, the Philippines is endemic to these diseases: dengue, chikungunya, and malaria. These diseases exhibit similar symptoms that even medical experts sometimes experience difficulty in differentiating without confirmatory laboratory testing. Dengue is the most prominent mosquito-borne viral disease rapidly spreading in the world; in the Philippines, dengue remains an ongoing public health concern. Another life-threatening mosquito-borne disease is malaria. Despite the Philippines being on the verge of attaining malaria-free status, there are still areas where malaria morbidity is high. Whereas, thousands of suspected chikungunya cases are reported every year by the Department of Health, Philippines; these numbers could be higher still, for it is often misdiagnosed as the acute phase of dengue due to similar clinical manifestations. Moreover, health services are continuously declining. The lack of equipment in health centers especially in rural areas, and favorable salaries and working conditions abroad, are some of the main reasons why despite the current situation, doctors migrate to developed countries; thus, there is a high disparity between the number of doctors and patients in the Philippines. This consequently poses a threat to the quality of healthcare provided to the Filipino people.
Computer programs such as clinical decision support system and expert system, have been developed to mimic the thought processes of physicians as they make diagnosis. As such, there is a study which aids people to determine if they have dengue; while another study, focuses on developing a decision algorithm which concludes whether a patient suffering from dengue needs to be hospitalized or not. In India, a decision support system diagnosing between malaria and dengue has been developed, implementing fuzzy logic with MATLAB. In the Philippines, there have been several medical expert systems developed primarily for herbal remedies, poison detection, and ears, nose, and throat disorders.
Health services are continuously declining. Because of understaffing of health care practitioners, particularly in public hospitals and rural areas, inadequate proper medical attention is given to patients. The rising proportions of Filipinos dying due to the misdiagnosis and the late diagnosis of viral mosquito-borne diseases exemplifies the need to create an expert system which could give timely preliminary diagnoses.
The proponents of the study aim to develop a Raspberry Pi-based expert system as a medical aid for the preliminary diagnosis of mosquito-borne diseases, namely, chikungunya, dengue, and malaria. The specific objectives of this study are (1) to create a fuzzy logic-based expert system with weighted rules; (2) to build a prototype integrating the expert system with a human-machine interface, a temperature sensor and an electronic blood pressure monitor with built-in pulse rate sensor; and (3) to test the said system and validate its accuracy on patients.
This research provides assistance to physicians in accomplishing the task of attending to patients’ health care and treatment especially in the underdeveloped parts of the country. The system's database will be used to create a programmatic and physical integration of logic, data, and choice - a medical expert system in which it imitates an expert human reasoning and advice. Using relevant information, it will then assess the patient’s wellness to provide a pre-diagnosis of the likelihood of having dengue, chikungunya and malaria. This is beneficial in rural areas and public hospitals where there is understaffing of health care practitioners such as doctors and nurses. In addition, this study is beneficial in the field of research for it could be utilized to develop an expert system with a wider domain.
The study only covers the mosquito-borne diseases dengue, malaria, and chikungunya. It will provide a preliminary diagnosis of the three mosquito-borne diseases by computing the percentage of likelihood of the patient to be suffering the three diseases based on the gathered information from patient such as symptoms as well as, if available, recent travel history (within the Philippines). The expert system uses a Raspberry Pi to integrate both the hardware and software. The system acts as an aid to the doctor, however, the doctor can overrule the expert system’s pre-diagnosis. In the case of dengue, the system cannot identify the stage at which the disease is occurring nor can it conduct further calculations to identify the stage after a patient is suspected to be suffering from dengue. The expert system can function independently and does not require medical personnel to operate it, however, assistance may still be given to the patient. The testing of the system would primarily be on patients from Zambales.