ANALİTİK HİYERARŞİ SÜRECİ VE VERİ MADENCİLİĞİ TEKNİKLERİYLE HİBRİT BİR KARAR DESTEK SİSTEMİ UYGULAMASI: TAM KAN SAYIMI DEĞERLERİ İLE KOVİD19 TANISI

Abstract

Objective: Data mining techniques have a significant impact on enhancing the precision of diagnostics based on artificial intelligence. In this research, it was aimed to develop a web-based decision support that predicts the status of a person who comes to the hospital with Covid-19 suspicion by using complete blood count results until the imaging and PCR test results are obtained. Method: In this study, firstly data pre-processing techniques on the data set were applied, then feature selection was made using data mining approaches. After reducing the number of variables, the analytical hierarchy process method (AHP), a prominent multi-criteria decision-making approach, was utilized. Through the AHP method combined with expert opinions, the priorities of the variables determined by machine learning were ascertained, leading to the development of a decision model using publicly accessible data. A web-based application of this decision model was subsequently crafted to provide the decision support system to the end-users. Furthermore, an evaluation was conducted to gauge the usability of the decision support system and the satisfaction of its users. Results: RFE-SVM feature selection algorithm identified seven pivotal variables: Basophil, Eosinophil, Lymphocyte, Leukocyte, Neutrophil, Platelet, and Monocyte. Consultations were held with six expert physicians spanning diverse specialties relevant to COVID-19 diagnosis decision-making with the AHP method. Out of the 42 expert users (57.1% were male, with an average age of 37.30±10.56) were evaluated the system. The System Usability Scale (SUS) score averaged 81.43±15.64, indicating high usability. Conclusion: Consequently, this system might enable faster isolation of the patient and the commencement of preliminary treatment.