TY  - JOUR
TI  - A Linear Stochastic System Approach to Model Symptom Based Clinical Decision Support Tool for the Early Diagnosis for Psoriasis, Seborrheic Dermatitis, Rosacea and Chronic Dermatitis
AB  - Prediction models provide the probability of an event. These models can be used to predict disease’s outcomes, reccurencies after treatments. Thispaper presents an expert system called Symptom Based Clinical Decision Support Tool (SBCDST) for early diagnosis of erythemato-squamous diseasesincorporating decisions made by Bayesian classification algorithm. This tool enables family practitioners to differentiate four types of erythematosquamousdiseases using clinical parameters obtained from a patient. In SBCDST, Psoriasis, Seborrheic Dermatitis, Rosacea and Chronic dermatitisdiseases are described by means of well-classified set of attributes. Attributes are generated from the typical sign and symptoms of disorder. Based onour clinical results, tool yields 72%, 93%, 89% and 95% correct decisions on the selected dermatology diseases respectively. System proposed will providethe opportunity for early diagnosis for the patient and the expert medical doctor to take the necessary preventive measures to treat the disease; andavoid malpractice which may cause irreversible health damages.
AU  - ZAİM GÖKBAY, İNCİ
AU  - ZİLELİ, Zeynep Beyza
AU  - YARMAN, Sıddık
AU  - SARI, Pelin
AU  - AKSOY, Türker Togay
DO  - 10.26650/electrica.2018.081118
PY  - 2019
JO  - Electrica
VL  - 19
IS  - 1
SN  - 2619-9831
SP  - 48
EP  - 58
DB  - TRDizin
UR  - http://search/yayin/detay/314629
ER  -