Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2521
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTharushika, G. K. A . A-
dc.contributor.authorRasanga, D. M. T-
dc.contributor.authorWeerathunge, I-
dc.contributor.authorBandara, P-
dc.date.accessioned2022-05-30T07:52:16Z-
dc.date.available2022-05-30T07:52:16Z-
dc.date.issued2021-07-01-
dc.identifier.citation. K. A. A. Tharushika, D. M. T. Rasanga, I. Weerathunge and P. Bandara, "Machine Learning-Based Skin And Heart Disease Diagnose Mobile App," 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2021, pp. 1-5, doi: 10.1109/ECAI52376.2021.9515126.en_US
dc.identifier.isbn978-1-6654-2534-6-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2521-
dc.description.abstractThis research aims to develop a Mobile app for predicting major diseases we have to face nowadays. These days the heart disease is the main source of death around the world. It is a complex task to predict a heart attack with a doctor because more experience and knowledge are needed. Sometimes it may be gastritis or asthma symptoms. Also, the following most common disease is a skin disease. Most people have some skin disease, and they don’t even have time to check it from a medical centre. These diseases led to deadly cancers kind of things. Implementing the Smart health care application, the skin disease classification and treatment, and the heart disease predictions can be made domestically. The application is taken images of skin disease through the device camera. It classifies the disease with the Keras ResNet trained to classify the accuracy as eighty-seven point eighty-three as a percentage. The heart disease prediction module takes 14 different attributes that can access by the personal and predict the heart disease probability with the model of sklearn KNeighborsClassifier is trained as a percentage with an accuracy of eighty-three point nine. The application was developed on top of the android platform with the SQL Lite database integration.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);Pages 1-5-
dc.subjectMachine Learningen_US
dc.subjectBased Skinen_US
dc.subjectHeart Diseaseen_US
dc.subjectDiagnoseen_US
dc.subjectMobile Appen_US
dc.titleMachine Learning-Based Skin And Heart Disease Diagnose Mobile Appen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ECAI52376.2021.9515126en_US
Appears in Collections:Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

Files in This Item:
File Description SizeFormat 
Machine_Learning-Based_Skin_And_Heart_Disease_Diagnose_Mobile_App.pdf
  Until 2050-12-31
385.71 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.