# Health Symptom Checking System for Elderly People Using Fuzzy Analytic Hierarchy Process
## 3 Approach
### Symptom Checker  
To develop an accessible system for the elderly, the medical knowledge of symptoms associated with the different diseases provided and assessed by [PERSON] and [PERSON] (co-authors of this paper), was stored in the backend database of the proposed app H2U. There were 13 different disease conditions associated with more than 50 symptoms in the developed system. For an illustration of the application of MCDA and fuzzy AHP methods, we solicited 7 diseases (mental illness, diabetes, skin, eye, dental, ortho, and cardiac), which are commonly suffered by the elderly, to implement the system. For example, diabetes has possible symptoms such as excessive third, frequent urination, weight loss, tiredness, and slow healing of wounds. Mental illness shows symptoms of depression, chronic anxiety, change in sleeping, extreme mood change, Alzheimer's disease, Parkinson's disease, seizure epilepsy, etc. To use the symptom checker, users need to create an account with their personal credentials, then a one-time password will be sent to the registered mobile number. Figure 3 shows the flowchart of the symptom checker. Once the account is created, the users will be able to choose different symptoms related to their ailments. After the symptom selection is done, the system will generate possible ailments and suggest clinical departments, which can help the users find effective tactics of treatments. This platform will also assist caregivers in having knowledge of the geriatric patients' ailment for the better treatment of their health issues. During a medical emergency, the users can make emergency calls to 911 for further proactive assistance. Steps for using MCDA fuzzy AHP in symptom checker are described below:  
* Identify the problems of various criteria, i.e., symptoms, with respect to the ailments. * Multiple inputs of problems are associated to form a consolidated outcome of different conditions of diseases. * Generating a set of pairwise comparison matrices of various diseases and symptoms based on the importance and choices made by the end-users. * The alternative with the highest overall score from the comparisons to the weight priorities of the symptoms is chosen by the patients as the final decision. * Based on the final decision from the score, the patients will be informed the specific doctors or departments being consulted. Figure 4 shows the mobile app interface where they choose the significant features of various symptoms for the medical interventions. Figure 4a shows the interface of the developed H2U system, which is composed of many useful functions related to healthcare, such as symptom checker, insurance, first aid, emergency support, assistance, medication, and medical report. After clicking the symptom checker icon, the app will show a list of symptoms for the user to choose from, as shown in Figure 4b. Based on the chosen symptoms, a list of possible diseases ranking in descending order will be displayed on the screen, as shown in Figure 4c. The app aims to provide an informational diagnosis tool with a medical knowledge base to make decisions by sorting the diseases based on the problems and health conditions. Therefore, the proposed symptom checker can prevent and treat the diseases of various symptoms with the right precision. This tool seeks to achieve a general implication, but is not a substitute for medical intervention or advice. Figure 3: Flow execution of symptom checker. The collected clinical data are stored in the app and transmitted to the central database server immediately or periodically through the internet. In case of emergency, the H2U healthcare system can send an alarm or trigger an alert to the physicians, their relatives, and caregivers for the timely action of the user. Once the alert message is triggered, the physician will be able to be ready for an emergency backup for the patient and in the meantime, the physician can also review the patient's clinical reports from the submitted medical information of the patient's database that was already stored in the cloud.