PerCIST: A Perceptual Computing-Based Decision Support System for Nonclinical Diagnosis of Diabetes Mellitus

Taniya Seth, Priyanka Mehra, Pranab K. Muhuri

Abstract: Diabetes mellitus (DM) is a disease impacting the regular activities and lifestyle of a majority of the human population throughout the world. Due to its adverse effects on health, regular interactions with healthcare providers become essential for early diagnosis, prognosis, and proper treatment plans. Such interactions often involve qualitative information, which when considered promotes effective and practical diagnosis. This is achieved in this article through the proposal of PerCIST, which is a perceptual computer-based nonclinical diagnostic tool for the prediction of DM. The proposed model is trained using cognitive understandings of individuals and takes into account the qualitative responses toward the parameters: age, body mass index, fasting blood sugar levels, HbA1c results, and heredity. These parameters, which are usually taken into account by medical practitioners for the diagnosis of DM, are decided through consultation of experts. The proposed model of PerCIST attains an accuracy of 93%, which is higher than any existing congeneric model, when tested on multiple individuals. Additional statistical and empirical experiments also highlight the prowess of the proposed model based on the significance of the chosen diagnostic parameters. Consequently, web and Android based applications, made freely available, are also provided to benefit people getting diagnosed with DM at home, especially during times of a pandemic when the entire world is under lockdown.

External IDs:dblp:journals/tfs/SethMM25