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A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data
Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh.ORCID iD: 0000-0002-7473-8185
Port City International University, Dhaka, Bangladesh.ORCID iD: 0000-0002-5602-9510
Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh.ORCID iD: 0000-0002-0084-0179
Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh.ORCID iD: 0000-0001-5844-6388
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2023 (English)In: Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings, part 2 / [ed] Md. Shahriare Satu; Mohammad Ali Moni; M. Shamim Kaiser; Mohammad Shamsul Arefin; Mohammad Shamsul Arefin, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 1, p. 271-282Conference paper, Published paper (Refereed)
Abstract [en]

Schizophrenia is a severe neurological disease where a patient’s perceptions of reality are disrupted. Its symptoms include hallucinations, delusions, and profoundly strange thinking and behavior, which make the patient’s daily functions difficult. Despite identifying genetic variations linked to Schizophrenia, causative genes involved in pathogenesis and expression regulations remain unknown. There is no particular way in life sciences for diagnosing Schizophrenia. Commonly used machine learning and deep learning are data-oriented. They lack the ability to deal with uncertainty in data. Belief Rule Based Expert System (BRBES) methodology addresses various categories of uncertainty in data with evidential reasoning. Previous researches showed the association of DNA methylation (DNAm) with risk of Schizophrenia. Whole blood DNAm data, hence, is useful for smart diagnosis of Scizophrenia. However, to our knowledge, no previous studies have investigated the performance of BRBES to diagnose Schizophrenia. Therefore, in this study, we explore BRBES’ performance in diagnosing Schizophrenia using whole blood DNAm data. BRBES was optimized by gradient-free algorithms due to the limitations of gradient-based optimization. Classification thresholds were optimized to yield better results. Finally, we compared performance to two machine learning models after 5-fold cross-validation where our model achieved the highest average sensitivity (76.8%) among the three.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 1, p. 271-282
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211, E-ISSN 1867-822X ; 491
Keywords [en]
BRBES, disjunctive BRBES, Dna methylation data, Scizophrenia
National Category
Other Computer and Information Science
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-99536DOI: 10.1007/978-3-031-34622-4_21Scopus ID: 2-s2.0-85164141484ISBN: 978-3-031-34621-7 (print)ISBN: 978-3-031-34622-4 (electronic)OAI: oai:DiVA.org:ltu-99536DiVA, id: diva2:1787242
Conference
1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022
Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-09-05Bibliographically approved

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Islam, Raihan UlAndersson, Karl

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