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A Belief Rule Based...
A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data
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- Hossain, Mohammad Shahadat (författare)
- Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh
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- Ahmed, Mumtahina (författare)
- Port City International University, Dhaka, Bangladesh
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- Raihan, S. M. Shafkat (författare)
- Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh
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- Sharma, Angel (författare)
- Department of Computer Science and Engineering, University of Chittagong, Chittagong, 4331, Bangladesh
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- Islam, Raihan Ul (författare)
- Luleå tekniska universitet,Datavetenskap
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- Andersson, Karl (författare)
- Luleå tekniska universitet,Datavetenskap
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(creator_code:org_t)
- Springer Science and Business Media Deutschland GmbH, 2023
- 2023
- Engelska.
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Ingår i: Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings, part 2. - : Springer Science and Business Media Deutschland GmbH. - 9783031346217 - 9783031346224 ; , s. 271-282
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- BRBES
- disjunctive BRBES
- Dna methylation data
- Scizophrenia
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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