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Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness

  • S.I. : Information, Intelligence, Systems and Applications
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Abstract

Dense neural networks (DNNs) are a powerful class of learning algorithms that uses multiple layers to progressively extract higher level features from raw input. Either deep or shallow, their outstanding capabilities made a very significant impact on improving the diagnostic potential in multiple applications including medical data classification. In this research work, DNN and Machine Learning (ML) models are explored to address the diagnosis problem of knee osteoarthritis classification which is a common complex problem in older adults. Knee OA diagnosis is a highly complex problem being related to a large number of medical risk factors including advanced age, gender, hormonal status, body weight or size, family history of disease, etc. The main research objective of this study is to apply DNN in knee osteoarthritis classification and validate it for the first time with respect to both accuracy and fairness. To accomplish this, a hybrid criterion including accuracies, confusion matrix and two fairness metrics (demographic parity (DP) and balanced equalized odds (BEO)) were employed to validate the performance of the proposed methodology. Different subgroups of control participants from self-reported clinical data were considered to prove the performance of the proposed methodology. The best performing DNN method is compared with some popular and well-known machine learning techniques for classification with respect to accuracy and fairness. The results of the conducted experimental analysis show the efficacy of the proposed DNN approach improving the classification accuracy (up to 79.6%) and fairness (BEO: ~ 92% and DP: 98.5%) in the OA case study.

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Acknowledgements

Part of this work has received funding from the European Community’s H2020 Programme, under grant agreement Nr. 777159 (OACTIVE).

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Authors and Affiliations

  1. AIDEAS OÜ, Narva mnt 5, Tallinn, Harju maakond, Estonia

    Serafeim Moustakidis

  2. Energy Systems Department, University of Thessaly, Geopolis Campus, 41500, Larissa, Greece

    Nikolaos I. Papandrianos, Eirini Christodolou & Elpiniki Papageorgiou

  3. Institute of Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, Papanastasiou 51, Larissa, Greece

    Elpiniki Papageorgiou & Dimitrios Tsaopoulos

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  1. Serafeim Moustakidis
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  2. Nikolaos I. Papandrianos
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  3. Eirini Christodolou
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  4. Elpiniki Papageorgiou
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  5. Dimitrios Tsaopoulos
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Correspondence to Serafeim Moustakidis.

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Moustakidis, S., Papandrianos, N.I., Christodolou, E. et al. Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Comput & Applic 35, 21–33 (2023). https://doi.org/10.1007/s00521-020-05459-5

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