IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i22p3515-d1518269.html
   My bibliography  Save this article

Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application

Author

Listed:
  • Mohammad Abu Tareq Rony

    (Department of Statistics, Noakhali Science & Technology University, Noakhali 3814, Bangladesh
    These authors contributed equally to this work.)

  • Fatama Tuz Johora

    (Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
    Applied INTelligence Lab (AINTLab), Seoul 05006, Republic of Korea)

  • Nisrean Thalji

    (Faculty of Computer Studies, Arab Open University, Amman 11953, Jordan)

  • Ali Raza

    (Department of Software Engineering, University of Lahore, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • Norma Latif Fitriyani

    (Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
    These authors contributed equally to this work.)

  • Muhammad Syafrudin

    (Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea)

  • Seung Won Lee

    (Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
    Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Republic of Korea
    Personalized Cancer Immunotherapy Research Center, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
    Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis.

Suggested Citation

  • Mohammad Abu Tareq Rony & Fatama Tuz Johora & Nisrean Thalji & Ali Raza & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application," Mathematics, MDPI, vol. 12(22), pages 1-27, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3515-:d:1518269
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/22/3515/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/22/3515/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    2. Vincenzo Verardi, 2013. "Semiparametric regression in Stata," United Kingdom Stata Users' Group Meetings 2013 14, Stata Users Group.
    3. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 61(01), pages 1-14, February.
    4. Shirin Enshaeifar & Ahmed Zoha & Andreas Markides & Severin Skillman & Sahr Thomas Acton & Tarek Elsaleh & Masoud Hassanpour & Alireza Ahrabian & Mark Kenny & Stuart Klein & Helen Rostill & Ramin Nilf, 2018. "Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    5. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    6. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    7. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    8. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    9. Xiaowan Liu & Zongxue Xu & Dingzhi Peng, 2019. "Spatio-Temporal Patterns of Vegetation in the Yarlung Zangbo River, China during 1998–2014," Sustainability, MDPI, vol. 11(16), pages 1-11, August.
    10. Hervé Cardot & Antonio Musolesi, 2019. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," SEEDS Working Papers 0219, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2019.
    11. Silvia L. P. Ferrari & Giovana Fumes, 2017. "Box–Cox symmetric distributions and applications to nutritional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 321-344, July.
    12. Olivia D�Aoust & Olivier Sterck & Philip Verwimp, 2013. "Buying Peace: The Mirage of Demobilizing Rebels," HiCN Working Papers 145, Households in Conflict Network.
    13. Mia Hubert & Irène Gijbels & Dina Vanpaemel, 2013. "Reducing the mean squared error of quantile-based estimators by smoothing," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 448-465, September.
    14. Małgorzata Kobylińska, 2021. "Spatial Diversity of Organic Farming in Poland," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    15. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
    16. Graciela Boente & Matías Salibian-Barrera, 2015. "S -Estimators for Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1100-1111, September.
    17. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 437-462, June.
    18. Fábio M. Bayer & Francisco Cribari‐Neto & Jéssica Santos, 2021. "Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 453-481, November.
    19. Vilca, Filidor & Santana, Lucia & Leiva, Víctor & Balakrishnan, N., 2011. "Estimation of extreme percentiles in Birnbaum-Saunders distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1665-1678, April.
    20. Pietro Coretto, 2022. "Estimation and computations for Gaussian mixtures with uniform noise under separation constraints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 427-458, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3515-:d:1518269. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.