Research Article
Diagnosis System of Toddler Diseases Using Forward Chaining and Case-Based Reasoning
@INPROCEEDINGS{10.4108/eai.24-10-2018.2280501, author={Indah Werdiningsih and Aisyah Shofiyyah Asma and Rimuljo Hendradi and Kartono Kartono and Purbandini Purbandini and Barry Nuqoba and Elly Anna}, title={Diagnosis System of Toddler Diseases Using Forward Chaining and Case-Based Reasoning}, proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus}, publisher={EAI}, proceedings_a={ICCSET}, year={2018}, month={11}, keywords={case-based reasoning forward chaining nearest neighbour similarity minkowsky distance similarity euclidean distance similarity}, doi={10.4108/eai.24-10-2018.2280501} }- Indah Werdiningsih
Aisyah Shofiyyah Asma
Rimuljo Hendradi
Kartono Kartono
Purbandini Purbandini
Barry Nuqoba
Elly Anna
Year: 2018
Diagnosis System of Toddler Diseases Using Forward Chaining and Case-Based Reasoning
ICCSET
EAI
DOI: 10.4108/eai.24-10-2018.2280501
Abstract
Toddlers are children aged 12-36 months. This study aims to diagnose toddler diseases using forward chaining and Case-Based Reasoning (CBR). There are 16 types of toddler diseases. This study consist of two steps, i.e., diagnosis using forward chaining and diagnosis using CBR. Diagnosis using forward chaining generated 18 rules. These rules were used to determine toddler diseases type, and diagnosis using CBR focused on three types of CBR calculations, i.e., Nearest Neighbour Similarity (NNS), Minkowsky Distance Similarity (MDS), and Euclidean Distance Similarity (EDS). The results of system testing using 600 data, the accuracy of diagnosis were 82% and 90%, using forward chaining and CBR respectively. Based on these results, diagnosis using CBR was better than forward chaining, because CBR justified the data that were considered wrong, to be repaired by an expert and then made them as new cases
