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MediCare – Bridging the gap between the rural people andhealthcare facilitiesS. Sathish Kumar? 1T.Vignesh? 4? ? M.E.R. Vignesh? 2S. Saravanan? 3Computer Science & EngineeringS.A. Engineering College, Chennai, IndiaAbstractRural Health care is one of the biggest challenges faced by the Health Ministryof India. With more than 70 percent people living in rural side integrated with low levelhealth facilities, mortality rates due to diseases are on a high. The main cause for thisaugmented mortality rate is mainly due to the lack of finding a resource for medicalfacilities. Hence, the core idea of ‘Medicare’ is to overcome this problem by facilitatingthe rural people with an Android Application which will be made available in a Kiosk.The key feature of this Android application will be to get the symptoms of aperson and predict the possible diseases with the respective criticality level, fetch thelocation of the user & show the nearby doctor’s location on Google Maps1, Providethe location of specialist doctors for a critical disease. The patient’s details will bemade available on the Doctor’s Login.Keywords- ? Prediction of diseases, Criticality Level, Kiosk, Google Maps, SpecialisedDoctor’s location, Patient details on Doctor’s Login.1. IntroductionAmong 121 crore Indians, Rural areas were occupied by 83.3 crore people andurban areas by 37.7 crore people, stated by Provisional Population Totals of Rural-UrbanDistribution in the year 2011, published by Union Home Secretary R.K. Singh. The needfor medical attention is high in rural areas. Thus the idea of this application is facilitatingthe rural people with an Android Application which will be made available in a Kiosk.The application will be connected to an Application programing interface15which will analyse the entered data and provide the possible diseases to the user. It’ll alsotell that person about the specialist doctors13. Thus, it ensures that the people in therural areas will know the exact location of the doctors by which they can be diagnosed.2. LITERATURE SURVEYThe paper presented by Cem Tekin, (Member, IEEE), Onur Atan, and MihaelaVan Der Schaar, (Fellow, IEEE) provision an expert selection system that learns onlinethe best expert to assign to each patient depending on the context of the patient13. Ingeneral, the context can include an enormous number and variety of information relatedto the patient’s health condition, age, gender, previous drug doses.The paper presented by Dr. Mahboob Khan (PHD) implements good HealthPrediction system using Data-Mining 2. Data processing could be a technology thatuses already existing knowledge within the info to govern results. The massive set ofinformations are processed by extracting and processing data sets. The informations areloaded with various diseases, their symptoms and medicines. The symptoms are predictedby the user dealt with it. All the symptoms are processed by the system and the output isgenerated for most probable one.Naive Thomas Bayes Model for likelihood estimation3, was created by Daniel lowdand Pedro Domingos pointed at large datasets, Accuracy and learning time is efficient inNaive Thomas when compared with other Theorem Networks. The size and extent ofNaive Thomas Bayes illation is greater than Theorem Network illation.A recent Survey on Health Care Prediction portrays that data processing is themotivational unit for any data Health Care Organization. The extraction of data ishandled by automatic or semi-automatic means that, totally different areas of miningembody clump, prediction, path analysis.3. Proposed MethodologyMedicare is an application which will have a patient login and a doctor login. Afterregistering the required informations login entry is created for every patients. Thespecification of various symptoms are recorded and the application will take a surveyrelating his/her illness. Thus, it will analyse the data gathered to provide the possiblediseases with the specialist details. After viewing the list of specialist doctors, a user canfix his appointment.Now, the doctor can login into his account with his login credentials and view thepatient’s location with his details about the patient and the appointment.4. ImplementationThere will be a kiosk present in the village which will have the ‘Medicare’Application installed in a Smart device. The patient will need to register by giving hisdetails. After registration, the patient can login into his account. Initially, the symptomswill be fetched from a remote server6 which will have the API11 (Applicationprogramming interface) for analysing the symptoms that are fed by the patients whichwill also be linked with a database12.At first, the list of all symptoms will be retrieved as a JSON (JavaScript ObjectNotation) 5 data which will then be rendered into the android application.Symptoms as JSON data retrieved from serverAfter rendering, the user can enter his symptoms using a list of select boxes. Aftersubmitting the symptoms, the application will prompt with a survey to collect themetadata about the symptoms. Based on the survey, an additional set of symptoms can beretrieved from the server by which it is related to the already collected symptoms inorder to refine the diagnosis.Additional symptom data based on the surveyThe data that was collected from the patient is then sent to the server7 foranalysing. The data will be analysed into a set of decision tree89 which will then senda possible disease10 to the client. The analysing of the symptoms include decision treeanalysis to get the most probable disease that might have occured based on thesymptoms. The decision tree induction is to create a decision tree that corresponds to thecollected data16.JSON data will be retrieved which will be rendered to the Android application bywhich the user can view the details of the diseases.Possible diseases with its detailsAfter predicting the possible diseases by using the list of symptoms’ data andmetadata collected using the survey, the app will receive a list of suggestedspecialisations for calculated diseases.specialisations for calculated diseasesThe application also has a Red flag feature. Red flag texts are recommendations tothe patient for a higher urgency or severeness of the possible symptoms. As an example apatient with pain in the breast might have a heart attack and therefore the patient shouldbe warned about the urgency and severeness of the matter.Red flag dataThe patient will now view the real-time location of the doctor who are nearby tothe patient’s current location (i.e.) The Kiosk location. Here’s where the paper ‘Discoverthe Expert: Context-Adaptive Expert Selection for Medical Diagnosis’ 1314 can beused to facilitate the patient with the most suitable specialist. Now, he can book anappointment.After the appointment is booked, the doctor can login into the application with hiscredentials by which he will be able to see the details of the patient and the fixedappointment.5. ConclusionIn this paper, we have discussed about bridging the gap between the rural peopleand the health care facility by using an Android application which will be made availablein the Smartphone in a Kiosk. The app can prove helpful in imperative cases whereverpatient is unable to find a doctor, for emergency cases that don’t have doctors in theregion, throughout late night emergencies and additionally for test of patients.References1 ‘Location Based Services and Integration of Google Maps in Android’ on theInternational Journal Of Engineering And Computer Science ISSN:2319 – 7242 Volume3 Issue 3 March,2014 Page No. 5072-5077.2 DR Mahboob Khan (PhD) “Smart Health Prediction using Data Mining” ,International Journal of Advanced Research in Computer Engineering & Technology(IJARCET).3 Daniel Lowd ,Pedro Domingos “Naïve Bayes Model For Probability Distribution”Department of Computer Science and Engineering, University of Washington, Seattle,WA 98195-2350, USA4 Sujatha, Sumathy, Anitha Nithya “A Survey of Health Care Prediction Using DataMining”, International Journal of Innovative Research in Science, Engineering andTechnology Vol. 5, Issue 8, August 2016.5 A Comprehensive analysis of XML and JSON web technologies by Zia Ul Haq, GulFaraz Khan, Tazar Hussain.6 ‘Designing a Cloud based Framework for HealthCare System and applying Clusteringtechniques for Region Wise Diagnosis’ on 2nd International Symposium on Big Data andCloud Computing (ISBCC’15)7 ‘An Overview of World Wide Web Protocol (Hypertext Transfer Protocol andHypertext Transfer Protocol Secure’ on International Journal of Advanced Research inComputer Science and Software Engineering.8 Conditional Probability Tree Estimation Analysis and Algorithms on 2009 by AlinaBeygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin and Alex Strehl.9 A. Blum, A. Kalai, and J. Langford. Beating the holdout: Bounds for k-fold andprogressive cross-validation, Proceedings of the 12th Annual Conference onComputational Learning Theory(COLT), 203–208, 1999.10 ‘Study of Heart Disease Prediction using Data Mining’ on International Journal ofAdvanced Research in Computer Science and Software Engineering.11 ‘Design and implementation of web based on Laravel framework’ on 2014International Conference on Computer Science and Electronic Technology(ICCSET2014)12 ‘ Comparative analysis of NoSQL (MongoDB) with MySQL Database’ onInternational journal for modern trends and research.13 ‘Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis’ byCem Tekin, (Member, IEEE), Onur Atan, and Mihaela Van Der Schaar, (Fellow, IEEE)on the IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING VOL:3JUNE:201514 H. Wang, W. Fan, P. S. Yu, and J. Han, ”Mining concept-drifting data streams usingensemble classifiers,” in Proc. 9th ACM SIGKDD Int. Conf. Knowl. Discovery DataMining, 2003, pp. 226–235.15 P., Mell, T., Grance. The NIST definition of cloud computing Online, Available:http://csrc.nist.gov/groups/SNS/cloud computing/cloud-def. v15.doc,2009. Accessed: 15-July- 2011.16 ‘Using decision tree classification to assist in the prediction of Alzheimer’s disease’in Computer Science and Information Technology (CSIT), 2014 6th InternationalConference.