Open AccessImplementation of Fuzzy-based Model for Prediction of Thalassemia Diseases
Author(s) -
er susanto,
Admi Syarif,
Kurnia Muludi,
rr perdani,
Agus Wantoro
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1751/1/012034
Subject(s) - thalassemia , novelty , fuzzy logic , computer science , medicine , disease , intermedia , artificial intelligence , pediatrics , psychology , social psychology , art , performance art , art history
Thalassemia is known as one of the blood disorder diseases that is inherited by parents. There are several types of Thalassemia, namely as Thalassemia major, minor, and intermedia. Among them, Thalassemia major is the most dangerous and needs more attention. Generally, it can be detected since the child is one year old. Late detection of this disease can have adverse consequences and various complications. This study aims to develop a new model for the prediction of thalassemia for children. The model adopts a fuzzy-based rule. The novelty in this article is that our model has 4 outputs, namely thalassemia major, intermedia, minor and not thalassemia. In the previous article it only had 3 outputs. In this study, we intend to implement a model that we developed using a fuzzy-based approach to classify thalassemia diseases based on CBC data. This article describes how to build a model and implement it in software. We compare the test results with the opinion of pediatricians regarding thalassemia. The final results of testing 4 CBC data show that our proposed model has successfully identified the type of thalassemia.