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

Year 2019, Volume: 19 Issue: 1, 48 - 58, 01.01.2019

İnci Zaim Gökbay Zeynep Beyza Zileli Pelin Sarı Türker Togay Aksoy Sıddık Yarman

Abstract

DOI: 10.26650/electrica.2018.081118

Prediction models provide the probability of
an event. These models can be used to predict disease’s outcomes, reccurencies
after treatments. This paper presents an expert system called Symptom Based
Clinical Decision Support Tool (SBCDST) for early diagnosis of
erythemato-squamous diseases incorporating decisions made by Bayesian
classification algorithm. This tool enables family practitioners to
differentiate four types of erythemato-squamous diseases using clinical
parameters obtained from a patient. In SBCDST, Psoriasis, Seborrheic
Dermatitis, Rosacea and Chronic dermatitis diseases are described by means of
well-classified set of attributes. Attributes are generated from the typical
sign and symptoms of disorder. Based on our clinical results, tool yields 72%,
93%, 89% and 95% correct decisions on the selected dermatology diseases
respectively. System proposed will provide the opportunity for early diagnosis
for the patient and the expert medical doctor to take the necessary preventive
measures to treat the disease; and avoid malpractice which may cause
irreversible health damages.

Cite this article as: Zaim Gökbay İ, Zileli
ZB, Sarı P, Aksoy TT, Yarman S. 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. Electrica,
2019; 19(1): 48-58.

Keywords

Clinical Decision Support Systems (CDSS), linear stochastic model, psoriasis, seborrheic dermatitis, rosacea and chronic dermatitis

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