Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction
Open accessAbstract
BACKGROUND: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. METHODS: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. RESULTS: For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. CONCLUSIONS: Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Researchers and clinicians are using artificial intelligence to integrate electronic health records and social risk factors, such as neighborhood characteristics, to predict outcomes important to individuals with diabetes, including poor control.
“researchers and clinicians are using artificial intelligence (AI) to integrate electronic health records (EHRs) and social risk factors, such as neighborhood characteristics, to predict outcomes important to individuals with diabetes, including poor control [17-22].”
From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [19]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011 · cited 1× · see citing works
“Effective preventive interventions such as lifestyle modification [15–17] and pharmacotherapy [17, 18] exist for type 2 diabetes.”
BackgroundTuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., Keinänen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200105033441801 · Cited by 10,873 · cited 1× · see citing works
“Effective preventive interventions such as lifestyle modification [15-17] and pharmacotherapy [17, 18] exist for type 2 diabetes.”
BackgroundKuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788 · cited 1× · see citing works
“often achieve the highest prediction accuracy [8].”
Pan, X.-R., Li, G.-W., Hu, Y.-H., Wang, J.-X., Yang, W.-Y., An, Z.-X., Hu, Z.-X., Juan-Lin, Xiao, J.-Z., Cao, H.-B., et al. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.537 · Cited by 4,320 · see citing works
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870 · see citing works
Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial
Chiasson, J.-L., Josse, R. G., Gomis, R., Hanefeld, M., Karasik, A., & Laakso, M. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08905-5 · Cited by 2,543 · see citing works
Lindström, J., & Tuomilehto, J. O. (2003) · Diabetes Care · doi:10.2337/diacare.26.3.725 · Cited by 1,889 · see citing works
Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491 · see citing works
CPAR: Classification based on Predictive Association Rules
Yin, X., & Han, J. (2003) · Proceedings of the 2003 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972733.40 · Cited by 833 · see citing works
Comprehensible classification models
Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 575 · see citing works
D, M. P. S. M., Williams, K., & Haffner, S. M. (2002) · Annals of Internal Medicine · doi:10.7326/0003-4819-136-8-200204160-00006 · Cited by 506 · see citing works
A review of associative classification mining
Thabtah, F. (2007) · The Knowledge Engineering Review · doi:10.1017/s0269888907001026 · Cited by 289 · see citing works
Heikes, K. E., Eddy, D. M., Arondekar, B. V., & Schlessinger, L. (2008) · Diabetes Care · doi:10.2337/dc07-1150 · Cited by 276 · see citing works
Lifetime Direct Medical Costs of Treating Type 2 Diabetes and Diabetic Complications
Zhuo, X., Zhang, P., & Hoerger, T. J. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.04.017 · Cited by 272 · see citing works
(2012) · Cited by 253 · see citing works
Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy
Asadi, H., Dowling, R., Yan, B., & Mitchell, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088225 · Cited by 218 · see citing works
Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2325615 · Cited by 133 · see citing works
Santos, R. L. T., Macdonald, C., & Ounis, I. (2015) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000040 · Cited by 118 · see citing works
Data mining for indicators of early mortality in a database of clinical records
Richards, G., Rayward-Smith, V. J., Sönksen, P. H., Carey, S., & Weng, C. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(00)00110-x · Cited by 118 · see citing works
Acceptance of Rules Generated by Machine Learning among Medical Experts
Mani, S., Shankle, W. R., & Pazzani, M. J. (2001) · Methods of Information in Medicine · doi:10.1055/s-0038-1634196 · Cited by 84 · see citing works
Luo, G., Tang, C., Yang, H., & Wei, X. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458104 · Cited by 77 · see citing works
Self-citationSimmons, R. K., Harding, A.-H., Wareham, N. J., Griffin, S. J., & on behalf of the EPIC‐Norfolk project team (2007) · Diabetic Medicine · doi:10.1111/j.1464-5491.2007.02173.x · Cited by 62 · see citing works
Predictive Modeling in Health Plans
Axelrod, R. C., & Vogel, D. S. (2003) · Disease Management & Health Outcomes · doi:10.2165/00115677-200311120-00003 · Cited by 48 · see citing works
Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods
Luo, G., Stone, B. L., Sakaguchi, F. H., Sheng, X., & Murtaugh, M. A. (2015) · JMIR Research Protocols · doi:10.2196/resprot.5039 · Cited by 26 · see citing works
Self-citationAutomatic Home Medical Product Recommendation
Luo, G., Thomas, S. B., & Tang, C. (2010) · Journal of Medical Systems · doi:10.1007/s10916-010-9483-2 · Cited by 20 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [19]Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. Proc KDD. 1998:80–6.
- [20]Li W, Han J, Pei J. CMAR: accurate and efficient classification based on multiple class-association rules. Proc ICDM. 2001: 369–76.
- [27]Luo G, Tang C, Yang H, Wei X. Med Search: a specialized search engine for medical information retrieval. Proc CIKM. 2008: 143–52.
Cited by
Sorted by citations
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834 · see citing works
Classification and prediction of diabetes disease using machine learning paradigm
Maniruzzaman, M., Rahman, M. J., Ahammed, B., & Abedin, M. M. (2020) · Health Information Science and Systems · doi:10.1007/s13755-019-0095-z · Cited by 389 · see citing works
A review of automatic selection methods for machine learning algorithms and hyper-parameter values
Luo, G. (2016) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-016-0125-6 · Cited by 357 · see citing works
Self-citationPayrovnaziri, S. N., Chen, Z., Rengifo‐Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa053 · Cited by 318 · see citing works
Human factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R., Hullman, J., & Bertini, E. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392878 · Cited by 219 · see citing works
A machine learning autism classification based on logistic regression analysis
Thabtah, F., Abdelhamid, N., & Peebles, D. (2019) · Health Information Science and Systems · doi:10.1007/s13755-019-0073-5 · Cited by 145 · see citing works
Niazi, S. K. (2023) · Drug Design Development and Therapy · doi:10.2147/dddt.s424991 · Cited by 146 · see citing works
Abhari, S., Kalhori, S. R. N., Ebrahimi, M., Hasannejadasl, H., & Garavand, A. (2019) · Healthcare Informatics Research · doi:10.4258/hir.2019.25.4.248 · Cited by 100 · see citing works
Big data management challenges in health research—a literature review
Wang, X., Williams, C., Liu, Z. H., & Croghan, J. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx086 · Cited by 85 · see citing works
Prognostication and risk factors for cystic fibrosis via automated machine learning
Alaa, A. M., & van der Schaar, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-29523-2 · Cited by 80 · see citing works
Wellner, B., Grand, J., Canzone, E., Coarr, M., Brady, P. W., Simmons, J. M., Kirkendall, E. S., Dean, N., Kleinman, M. E., & Sylvester, P. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.8680 · Cited by 78 · see citing works
Machine learning for diabetes clinical decision support: a review
Tuppad, A., & Patil, S. D. (2022) · Advances in Computational Intelligence · doi:10.1007/s43674-022-00034-y · Cited by 71 · see citing works
CatBoost Ensemble Approach for Diabetes Risk Prediction at Early Stages
Kumar, P. S., Anisha Kumari, K., Mohapatra, S., Naik, B., Nayak, J., & Mishra, M. (2021) · Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON) · doi:10.1109/odicon50556.2021.9428943 · Cited by 67 · see citing works
Wu, Y., Min, H., Li, M., Shi, Y., Ma, A., Han, Y., Gan, Y.-D., Guo, X., & Sun, X. (2023) · BMC Public Health · doi:10.1186/s12889-023-16066-z · Cited by 65 · see citing works
Luo, G., He, S., Stone, B. L., Nkoy, F. L., & Johnson, M. D. (2020) · JMIR Medical Informatics · doi:10.2196/16080 · Cited by 56 · see citing works
Self-citationA rule extraction approach from support vector machines for diagnosing hypertension among diabetics
Singh, N., Singh, P., & Bhagat, D. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.029 · Cited by 54 · see citing works
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Imrie, F., Davis, R. A., & van der Schaar, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00698-2 · Cited by 51 · see citing works
PredicT-ML: a tool for automating machine learning model building with big clinical data
Luo, G. (2016) · Health Information Science and Systems · doi:10.1186/s13755-016-0018-1 · Cited by 48 · see citing works
Self-citationLuo, G., Stone, B. L., Johnson, M. D., Tarczy-Hornoch, P. Z., Wilcox, A. B., Mooney, S. D., Sheng, X., Haug, P. J., & Nkoy, F. L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.7757 · Cited by 45 · see citing works
Self-citationHertroijs, D. F. L., Elissen, A., Brouwers, M. C. G. J., Schaper, N. C., Köhler, S., Popa, M. C., Asteriadis, S., Hendriks, S. H., Bilo, H. J. G., & Ruwaard, D. (2017) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13148 · Cited by 46 · see citing works
Kovalchuk, S. V., Kopanitsa, G., Derevitskii, I. V., Matveev, G. A., & Savitskaya, D. A. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104013 · Cited by 48 · see citing works
Lupus nephritis pathology prediction with clinical indices
Tang, Y., Zhang, W., Zhu, M., Zheng, L., Xie, L., Yao, Z., Zhang, H., Cao, D., & Lü, B. (2018) · Scientific Reports · doi:10.1038/s41598-018-28611-7 · Cited by 45 · see citing works
Machine learning approach on healthcare big data: a review
Supriya, M., & Deepa, A. (2020) · Big Data and Information Analytics · doi:10.3934/bdia.2020005 · Cited by 42 · see citing works
Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty
McLendon, P. B., Christmas, K. N., Simon, P., Plummer, O. R., Hunt, A., Ahmed, A. S., Mighell, M. A., & Frankle, M. A. (2021) · JBJS Open Access · doi:10.2106/jbjs.oa.20.00128 · Cited by 39 · see citing works
Lu, S., Chen, R., Wei, W., & Lu, X. (2021) · PubMed Central · doi:10.48550/arxiv.2103.11254 · Cited by 37 · see citing works
Caterson, J., Lewin, A., & Williamson, E. (2024) · Digital Health · doi:10.1177/20552076241272657 · Cited by 40 · see citing works
Tong, Y., Messinger, A. I., Wilcox, A. B., Mooney, S. D., Davidson, G. H., Suri, P., & Luo, G. (2021) · Journal of Medical Internet Research · doi:10.2196/22796 · Cited by 35 · see citing works
Self-citationLuo, G., Nau, C., Crawford, W., Schatz, M. X., Zeiger, R. S., Rozema, E. J., & Koebnick, C. (2020) · JMIR Medical Informatics · doi:10.2196/22689 · Cited by 34 · see citing works
Self-citationPham, T., Tao, X., Zhang, J., & Yong, J. (2020) · Health Information Science and Systems · doi:10.1007/s13755-020-0100-6 · Cited by 31 · see citing works
Machine learning classification approach for asthma prediction models in children
Ekpo, R. H., Osamor, V. C., Azeta, A. A., Ikeakanam, E., & Amos, B. O. (2023) · Health and Technology · doi:10.1007/s12553-023-00732-8 · Cited by 27 · see citing works
Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review
Fernández‐Alemán, J. L., de Gea, J. M. C., Hosni, M., Idri, A., & Garcı́a-Mateos, G. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856341 · Cited by 24 · see citing works
Tong, Y., Messinger, A. I., & Luo, G. (2020) · IEEE Access · doi:10.1109/access.2020.3032683 · Cited by 22 · see citing works
Self-citationInterpretable models to predict Breast Cancer
Ferreira, P. C., Dutra, I., Salvini, R. L., & Burnside, E. S. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822745 · Cited by 20 · see citing works
Diabetes prediction using Hybrid Bagging Classifier
Chandramouli, A., Hyma, V. R., Tanmayi, P. S., Santoshi, T. G., & Priyanka, B. (2023) · Entertainment Computing · doi:10.1016/j.entcom.2023.100593 · Cited by 19 · see citing works
Considerations for Identifying Social Needs in Health Care Systems
Nau, C., Adams, J., Roblin, D. W., Schmittdiel, J. A., Schroeder, E. B., & Steiner, J. F. (2019) · Medical Care · doi:10.1097/mlr.0000000000001173 · Cited by 18 · see citing works
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support SystemsPreprint
Lahav, O., Mastronarde, N., & van der Schaar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10799 · Cited by 18 · see citing works
Luo, G., Stone, B. L., Nkoy, F. L., He, S., & Johnson, M. D. (2019) · JMIR Medical Informatics · doi:10.2196/12591 · Cited by 17 · see citing works
Self-citationLuo, G., Johnson, M. D., Nkoy, F. L., He, S., & Stone, B. L. (2020) · JMIR Medical Informatics · doi:10.2196/21965 · Cited by 17 · see citing works
Self-citationXue, M., Su, Y., Li, C., Wang, S., & Yao, H. (2020) · Journal of Diabetes Research · doi:10.1155/2020/6873891 · Cited by 16 · see citing works
Yang, J., Ju, X., Liu, F., Asan, O., Church, T. R., & Smith, J. (2021) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2021.3117872 · Cited by 16 · see citing works
Liaw, W. R., Ramos Silva, Y., Soltero, E. G., Krist, A., & Stotts, A. L. (2023) · JMIR AI · doi:10.2196/45032 · Cited by 14 · see citing works
“To meet these goals, researchers and clinicians are using artificial intelligence (AI) to integrate electronic health records (EHRs) and social risk factors, such as neighborhood characteristics, to predict outcomes important to individuals with diabetes, including poor control [17-22].”
BackgroundA Comprehensive Review of Big Data Analysis Techniques in Health-Care
Tiwari, S. K., Kaur, J., Singla, P., & Hrisheekesha, P. N. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2271-0_32 · Cited by 14 · see citing works
Luo, G. (2019) · Global Transitions · doi:10.1016/j.glt.2018.11.001 · Cited by 14 · see citing works
Self-citationLuo, G., Stone, B. L., Koebnick, C., He, S., Au, D. H., Sheng, X., Murtaugh, M. A., Sward, K. A., Schatz, M. X., Zeiger, R. S., et al. (2019) · JMIR Research Protocols · doi:10.2196/13783 · Cited by 14 · see citing works
Self-citationRehman, N. U., Contreras, I., Beneyto, A., & Vehı́, J. (2024) · Mathematics · doi:10.3390/math12101567 · Cited by 13 · see citing works
Diabetes mellitus early stage risk prediction using machine learning algorithms
Samet, S., Laouar, M. R., & Bendib, I. (2021) · International Conference on Networking and Advanced Systems (ICNAS) · doi:10.1109/icnas53565.2021.9628955 · Cited by 13 · see citing works
Comparative study of Ensemble learning Algorithms on Early Stage Diabetes Risk Prediction
Banchhor, M., & Singh, P. (2021) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet51464.2021.9456263 · Cited by 12 · see citing works
A Roadmap for Optimizing Asthma Care Management via Computational Approaches
Luo, G., & Sward, K. A. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.8076 · Cited by 12 · see citing works
Self-citationDiabetes prediction using an improved machine learning approach
Lyaqini, S., & Nachaoui, M. (2021) · Mathematical Modeling and Computing · doi:10.23939/mmc2021.04.726 · Cited by 11 · see citing works
Taylor, A. K., Steeg, S., Quinlivan, L. M., Gunnell, D., Hawton, K. E., & Kapur, N. (2020) · BJPsych Open · doi:10.1192/bjo.2020.123 · Cited by 10 · see citing works
Luo, G., Nau, C., Crawford, W., Schatz, M. X., Zeiger, R. S., & Koebnick, C. (2021) · Journal of Medical Internet Research · doi:10.2196/24153 · Cited by 9 · see citing works
Self-citationHuang, T., Liu, S., Huang, J., Li, J., Liu, G., Zhang, W., & Wang, X. (2021) · Current Medical Research and Opinion · doi:10.1080/03007995.2021.2015156 · Cited by 9 · see citing works
Zeng, S., Arjomandi, M., & Luo, G. (2022) · JMIR Medical Informatics · doi:10.2196/33043 · Cited by 8 · see citing works
Self-citationŠtiglic, G., Wang, F., Sheikh, A., & Cilar, L. (2021) · Primary care diabetes · doi:10.1016/j.pcd.2021.04.008 · Cited by 8 · see citing works
Wang, Z. (2023) · Sustainability · doi:10.3390/su15054367 · Cited by 7 · see citing works
Patient-Specific Explanations for Predictions of Clinical Outcomes
Tajgardoon, M., Samayamuthu, M. J., Calzoni, L., & Visweswaran, S. (2019) · ACI Open · doi:10.1055/s-0039-1697907 · Cited by 7 · see citing works
Sajid, M. R., Khan, A. A., Albar, H. M., Muhammad, N., Sami, W., Bukhari, S. A. C., & Wajahat, I. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5475313 · Cited by 7 · see citing works
Rajaguru, H., & Chellappan, D. (2024) · Bioengineering · doi:10.3390/bioengineering11080766 · Cited by 7 · see citing works
Luo, G. (2021) · JMIR Medical Informatics · doi:10.2196/27778 · Cited by 6 · see citing works
Self-citationComparison of Performance Support Vector Machine Algorithm and Naive Bayes for Diabetes Diagnosis
Watomakin, D. B., & Emanuel, A. W. R. (2019) · International Conference on Science in Information Technology · doi:10.1109/icsitech46713.2019.8987464 · Cited by 6 · see citing works
Developing a system dynamics model for prediction of phosphorus in facultative stabilization ponds
Shahsavani, E., Ebrahimi, A. A., Ehrampoush, M. H., Maleknia, H., Eslami, H., & Samaei, M. R. (2019) · AMB Express · doi:10.1186/s13568-019-0882-6 · Cited by 4 · see citing works
Classification vs Clustering: Ways for Diabetes Detection
Singh, S., & Vazirani, V. (2022) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct54291.2022.9825053 · Cited by 4 · see citing works
Towards an explainable machine learning model to reduce readmission risks for diabetes patients
Guo, C., Zhou, H., Pires, I. M., Coelho, P. J., Tong, R., & Farid, F. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101686 · Cited by 3 · see citing works
Srinivasan, S., & Muruganandam, S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3284-9_13 · Cited by 3 · see citing works
The Role of Machine Learning in the Healthcare Sector: A Roadmap to the Potential Prospects
Xhaferra, E., & Ismaili, F. (2022) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora55278.2022.9800065 · Cited by 3 · see citing works
Zhang, X., & Luo, G. (2021) · JMIR Medical Informatics · doi:10.2196/28287 · Cited by 3 · see citing works
Self-citationModel-agnostic interpretation by visualization of feature perturbationsPreprint
Marc'ilio-Jr, W. E., Eler, D. M., & Breve, F. (2021) · arXiv.org · doi:10.48550/arxiv.2101.10502 · Cited by 1 · see citing works
Al-Zoubi, H. R., & Albzour, N. (2022) · Research Square · doi:10.21203/rs.3.rs-2381448/v1 · Cited by 3 · see citing works
Su, Y., & Bai, Y. (2025) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-025-04981-5 · Cited by 1 · see citing works
Comparative Study of ML-Based Diabetes Detection Using IoT and Lab Data in Fog
Xhaferra, E., Ismaili, F., & Cina, E. (2025) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2025.03.001 · Cited by 1 · see citing works
Anticipation in Medicine and Healthcare: Implications for Improving Safety and Quality
Staiger, T. O., Kritek, P., Luo, G., & Tarczy-Hornoch, P. Z. (2019) · Springer eBooks · doi:10.1007/978-3-319-91554-8_32 · Cited by 1 · see citing works
Self-citationLuo, G., Stone, B. L., Sheng, X., He, S., Koebnick, C., & Nkoy, F. L. (2021) · JMIR Research Protocols · doi:10.2196/27065 · Cited by 1 · see citing works
Self-citationExplaining Machine Learning Predictions in Botnet Detection
Miller, S. T., & Busby-Earle, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-23492-7_26 · Cited by 1 · see citing works
Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine
Dhan, A. R., & Kumar, B. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/mr24608013906 · Cited by 1 · see citing works
Discrete Hyperparameter Optimization Model Based on Skewed Distribution
Li, Y. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/2835596 · Cited by 1 · see citing works
Diagnosis of Female Diabetic Patients Based on Artificial Intelligence
Xu, X. (2019) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1109/aiam48774.2019.00091
Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model
Hsieh, K. (2019) · Digital Commons-TMC (Texas Medical Center)
Gestational Diabetes Prediction Using Machine Learning Algorithms
Bhagile, V. D., & Fathail, I. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-4538-9_6
Anticipation in Medicine and Healthcare: Implications for Improving Safety and Quality
Staiger, T. O., Kritek, P., Luo, G., & Tarczy-Hornoch, P. Z. (2017) · Springer eBooks · doi:10.1007/978-3-319-31737-3_32-1
Self-citationPostgraduate Teaching Reform Based on Machine Learning and Improved SVM Algorithm
(2021) · Machine Learning Theory and Practice · doi:10.38007/ml.2021.020205
Model-Agnostic Interpretation via Feature Perturbation Visualization
Marcílio-Jr, W. E., Eler, D. M., & Breve, F. (2023) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi59091.2023.10347141
Priyadarshani, P. P., Nayak, J., & Dash, P. B. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10478-2
Wu, Y., Min, H., Li, M., Shi, Y., Ma, A., Han, Y., Gan, Y., Guo, X., & Sun, X. (2023) · Figshare · doi:10.6084/m9.figshare.23665266
Optimizer Influenced Deep Neural Network for IoT Healthcare Security
Priyadarshani, P. P., Nayak, J., & Dash, P. B. (2026) · SN Computer Science · doi:10.1007/s42979-025-04657-z
Explainable Machine Learning Models for Type-2 Diabetes Prediction
Aslam, S., & Raza, U. A. (2026) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom69009.2026.11360888
A Novel Optimizer Based Deep Neural Network Model for IoMT
Sriveni, B., Navuri, N. K., Srinivas, K., Paruchuri, T., & Muthusamy, J. (2026) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot67589.2026.11455694
- Authors
- Luo, G.
- Affiliations
- University of Utah (US)
- Corresponding author
- Luo, Gang
- Venue
- Health Information Science and Systems
- Published
- 2016-03-08
- Volume
- 4
- Issue
- 1
- Pages
- 2-2
- ISSN
- 2047-2501 (ISSN-L)
- PMC
- PMC4782293
- OpenAlex
- W2295559204
- Cited by
- 85
- References
- 25
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 11 · 2022: 9 · 2021: 16 · 2020: 12 · 2019: 14 · 2018: 3 · 2017: 6 · 2016: 3
- FWCI
- 7.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Health Information Science and Systems (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable machine learning, type 2 diabetes prediction, model interpretability, electronic health records, diabetes classification, clinical decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
