{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:21:49Z","timestamp":1774966909427,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,17]]},"DOI":"10.1145\/3723178.3723228","type":"proceedings-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T07:16:47Z","timestamp":1749194207000},"page":"376-383","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8194-0779","authenticated-orcid":false,"given":"Aurora Lithe","family":"Roy","sequence":"first","affiliation":[{"name":"Institute of Engineering &amp; Management, Kolkata, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9986-210X","authenticated-orcid":false,"given":"Md Kamrul","family":"Siam","sequence":"additional","affiliation":[{"name":"Computer Science, New York Institute of Technology, New York, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9534-1761","authenticated-orcid":false,"given":"Nuzhat","family":"Prova","sequence":"additional","affiliation":[{"name":"Pace University, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2202-552X","authenticated-orcid":false,"given":"Abdullah","family":"Al Maruf","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Bangladesh University of Business &amp; Technology, Dhaka, Bangladesh"}]}],"member":"320","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Antonio Agliata Deborah Giordano Francesco Bardozzo Salvatore Bottiglieri Angelo Facchiano and Roberto Tagliaferri. 2023. Machine Learning as a Support for the Diagnosis of Type 2 Diabetes. International Journal of Molecular Sciences 24 7 (April 2023) 6775. 10.3390\/ijms24076775","DOI":"10.3390\/ijms24076775"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Gabriel Aguilera-Venegas Amador L\u00f3pez-Molina Gemma Rojo-Mart\u00ednez and Jos\u00e9\u00a0Luis Gal\u00e1n-Garc\u00eda. 2023. Comparing and tuning machine learning algorithms to predict type 2 diabetes mellitus. J. Comput. Appl. Math. 427 (Aug. 2023) 115115. 10.1016\/j.cam.2023.115115","DOI":"10.1016\/j.cam.2023.115115"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Usama Ahmed Ghassan\u00a0F Issa Muhammad\u00a0Adnan Khan Shabib Aftab Muhammad\u00a0Farhan Khan Raed\u00a0AT Said Taher\u00a0M Ghazal and Munir Ahmad. 2022. Prediction of Diabetes Empowered With Fused Machine Learning. IEEE Access 10 (2022) 8529\u20138538. 10.1109\/access.2022.3142097","DOI":"10.1109\/access.2022.3142097"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","unstructured":"T. Barrett D.\u00a0B. Troup S.\u00a0E. Wilhite P. Ledoux C. Evangelista I.\u00a0F. Kim M. Tomashevsky K.\u00a0A. Marshall K.\u00a0H. Phillippy P.\u00a0M. Sherman R.\u00a0N. Muertter M. Holko O. Ayanbule A. Yefanov and A. Soboleva. 2010. NCBI GEO: archive for functional genomics data sets\u201310 years on. Nucleic Acids Research 39 Database (Nov. 2010) D1005\u2013D1010. 10.1093\/nar\/gkq1184","DOI":"10.1093\/nar\/gkq1184"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Nikos Fazakis Otilia Kocsis Elias Dritsas Sotiris Alexiou Nikos Fakotakis and Konstantinos Moustakas. 2021. Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction. IEEE Access 9 (2021) 103737\u2013103757. 10.1109\/access.2021.3098691","DOI":"10.1109\/access.2021.3098691"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Shahid\u00a0Mohammad Ganie Majid\u00a0Bashir Malik and Tasleem Arif. 2022. Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches. Journal of Diabetes & Metabolic Disorders 21 1 (March 2022) 339\u2013352. 10.1007\/s40200-022-00981-w","DOI":"10.1007\/s40200-022-00981-w"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Jes\u00fas\u00a0Mar\u00eda Gonz\u00e1lez-Mart\u00edn Laura\u00a0B. Torres-Mata Sara Cazorla-Rivero Cristina Fern\u00e1ndez-Santana Estrella G\u00f3mez-Bentolila Bernardino Clavo and Francisco Rodr\u00edguez-Esparrag\u00f3n. 2023. An Artificial Intelligence Prediction Model of Insulin Sensitivity Insulin Resistance and Diabetes Using Genes Obtained through Differential Expression. Genes 14 12 (Nov. 2023) 2119. 10.3390\/genes14122119","DOI":"10.3390\/genes14122119"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Koushik\u00a0Chandra Howlader Md.\u00a0Shahriare Satu Md.\u00a0Abdul Awal Md.\u00a0Rabiul Islam Sheikh Mohammed\u00a0Shariful Islam Julian M.\u00a0W. Quinn and Mohammad\u00a0Ali Moni. 2022. Machine learning models for classification and identification of significant attributes to detect type 2 diabetes. Health Information Science and Systems 10 1 (Feb. 2022). 10.1007\/s13755-021-00168-2","DOI":"10.1007\/s13755-021-00168-2"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Orlando Iparraguirre-Villanueva Karina Espinola-Linares Rosalynn\u00a0Ornella Flores\u00a0Casta\u00f1eda and Michael Cabanillas-Carbonell. 2023. Application of machine learning models for early detection and accurate classification of type 2 diabetes. Diagnostics (Basel) 13 14 (July 2023).","DOI":"10.3390\/diagnostics13142383"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Ram\u00a0D. Joshi and Chandra\u00a0K. Dhakal. 2021. Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches. International Journal of Environmental Research and Public Health 18 14 (July 2021) 7346. 10.3390\/ijerph18147346","DOI":"10.3390\/ijerph18147346"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","unstructured":"Hanieh Karmand Aref Andishgar Reza Tabrizi Alireza Sadeghi Babak Pezeshki Mahdi Ravankhah Erfan Taherifard and Fariba Ahmadizar. 2024. Machine\u2010learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study. Endocrinology Diabetes & Metabolism 7 2 (Feb. 2024). 10.1002\/edm2.472","DOI":"10.1002\/edm2.472"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","unstructured":"Fayroza\u00a0Alaa Khaleel and Abbas\u00a0M Al-Bakry. 2023. Diagnosis of diabetes using machine learning algorithms. Materials Today: Proceedings 80 (2023) 3200\u20133203. 10.1016\/j.matpr.2021.07.196","DOI":"10.1016\/j.matpr.2021.07.196"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Haohui Lu Shahadat Uddin Farshid Hajati Mohammad\u00a0Ali Moni and Matloob Khushi. 2021. A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. Applied Intelligence 52 3 (June 2021) 2411\u20132422. 10.1007\/s10489-021-02533-w","DOI":"10.1007\/s10489-021-02533-w"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Naim\u00a0Al Mahi Mehdi\u00a0Fazel Najafabadi Marcin Pilarczyk Michal Kouril and Mario Medvedovic. 2019. GREIN: An interactive web platform for re-analyzing GEO RNA-seq data. Sci. Rep. 9 1 (May 2019) 7580.","DOI":"10.1038\/s41598-019-43935-8"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Karuna Middha and Apeksha Mittal. 2023. An effective feature selection method for type 2 diabetes mellitus detection using gene expression data. Intelligent Decision Technologies 17 3 (July 2023) 595\u2013606. 10.3233\/idt-220077","DOI":"10.3233\/idt-220077"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"Nurjahan Nipa Mahmudul\u00a0Hasan Riyad Shahriare Satu Walliullah Koushik\u00a0Chandra Howlader and Mohammad\u00a0Ali Moni. 2024. Clinically adaptable machine learning model to identify early appreciable features of diabetes. Intelligent Medicine 4 1 (Feb. 2024) 22\u201332. 10.1016\/j.imed.2023.01.003","DOI":"10.1016\/j.imed.2023.01.003"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Amos\u00a0Otieno Olwendo George Ochieng and Kenneth Rucha. 2023. Comparison of machine learning methods for the prediction of type 2 diabetes in primary care setting using EHR data. Journal of Agriculture Science and Technology 23 1 (Oct. 2023) 24\u201336. 10.4314\/jagst.v23i1.3","DOI":"10.4314\/jagst.v23i1.3"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Xiaoting Pei Di Qi Jiangman Liu Hongli Si Shenzhen Huang Sen Zou Dingli Lu and Zhijie Li. 2023. Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression. Scientific Reports 13 1 (April 2023). 10.1038\/s41598-023-34072-4","DOI":"10.1038\/s41598-023-34072-4"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Vivek Rai Daniel\u00a0X. Quang Michael\u00a0R. Erdos Darren\u00a0A. Cusanovich Riza\u00a0M. Daza Narisu Narisu Luli\u00a0S. Zou John\u00a0P. Didion Yuanfang Guan Jay Shendure Stephen\u00a0C.J. Parker and Francis\u00a0S. Collins. 2020. Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Molecular Metabolism 32 (Feb. 2020) 109\u2013121. 10.1016\/j.molmet.2019.12.006","DOI":"10.1016\/j.molmet.2019.12.006"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Aditya Saxena Nitish Mathur Pooja Pathak Pradeep Tiwari and Sandeep\u00a0Kumar Mathur. 2023. Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes. Biomolecules 13 3 (Feb. 2023) 432. 10.3390\/biom13030432","DOI":"10.3390\/biom13030432"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Nobuhiro Shojima and Toshimasa Yamauchi. 2023. Progress in genetics of type 2 diabetes and diabetic complications. Journal of Diabetes Investigation 14 4 (Jan. 2023) 503\u2013515. 10.1111\/jdi.13970","DOI":"10.1111\/jdi.13970"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Parvathaneni\u00a0Naga Srinivasu Jana Shafi T\u00a0Balamurali Krishna Canavoy\u00a0Narahari Sujatha S\u00a0Phani Praveen and Muhammad\u00a0Fazal Ijaz. 2022. Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data. Diagnostics 12 12 (Dec. 2022) 3067. 10.3390\/diagnostics12123067","DOI":"10.3390\/diagnostics12123067"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","unstructured":"Yurong Xin Jinrang Kim Haruka Okamoto Min Ni Yi Wei Christina Adler Andrew\u00a0J. Murphy George\u00a0D. Yancopoulos Calvin Lin and Jesper Gromada. 2016. RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes. Cell Metabolism 24 4 (Oct. 2016) 608\u2013615. 10.1016\/j.cmet.2016.08.018","DOI":"10.1016\/j.cmet.2016.08.018"}],"event":{"name":"ICCA 2024: 3rd International Conference on Computing Advancements","location":"Dhaka Bangladesh","acronym":"ICCA 2024"},"container-title":["Proceedings of the 3rd International Conference on Computing Advancements"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3723178.3723228","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3723178.3723228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:56:47Z","timestamp":1750298207000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3723178.3723228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":23,"alternative-id":["10.1145\/3723178.3723228","10.1145\/3723178"],"URL":"https:\/\/doi.org\/10.1145\/3723178.3723228","relation":{},"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"2025-06-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}