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• W4384927417 abstract "Millions of people die because of diabetes each year. Furthermore, most adults living with this condition are juggling with one or more other major health concerns. These related diseases also known as comorbidities, coexist with the primary disease, but also stand as their own specific disease. The challenge that healthcare professionals face is that Diabetes Mellitus (DM) is difficult to differentiate into its six forms. This hinders timely and accurate diagnosis and proper treatment. This paper presents our research in developing a novel Artificial Intelligence (AI) based approach to analyze data of real patients having different comorbidity diseases for interpretation and finding inferences for diagnosis and prognosis of DM and its comorbidities in patients in different scenarios. Details are provided about the data models used, relevant feature sets and their association rule mining, deep learning analytical models developed, and results validation against various accuracy measures. The performance of several big data analytics platforms was validated for the different models for three different sizes of endocrine datasets with varying parameters. The data models were mapped to HL7 FHIR v4 schema that is flexible in adapting to diagnostic models for all diseases. Out of several analytical models evaluated, Louvain Mani-Hierarchical Fold Learning (LMHFL) was found to be the most promising in terms of efficiency and accurate explainable diagnosis through reflective visualizations of associated features." @default.
• W4384927417 created "2023-07-22" @default.
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• W4384927417 date "2023-07-20" @default.
• W4384927417 modified "2023-09-27" @default.
• W4384927417 title "Rules Extraction, Diagnoses and Prognosis of Diabetes and its Comorbidities using Deep Learning Analytics with Semantics on Big Data" @default.
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• W4384927417 doi "https://doi.org/10.32388/67kz7s" @default.
• W4384927417 hasPublicationYear "2023" @default.
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