Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis by Maria A.C. Wester Trejo, Maryam Sadeghi, Shivam Singh, Naghmeh Mahmoodian, Samir Sharifli, Zdenka Hruskova, Vladimír Tesař, Xavier Puéchal, Jan Anthonie Bruijn, Georg Göbel, Ingeborg M. Bajema, Andreas Kronbichler

“…Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. …”
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Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models by Deok-Hee Kim-Dufor, Michel Walter, Marie-Odile Krebs, Yannis Haralambous, Philippe Lenca, Christophe Lemey, Christophe Lemey

“…A gradient-boosted decision tree algorithm was tested to evaluate its potential to correctly classify three categories of patients and find relevant linguistic markers at the level of lexical richness, semantic coherence, speech disfluency, and syntactic complexity. …”
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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson’s disease classification by Sameer Bhat, Piotr Szczuko

“…Four datasets are created from an original dataset: $$DS_0$$ (normal dataset), $$DS_1$$ ( $$DS_0$$ subjected to Canny edge detection and Hessian filtering), $$DS_2$$ (augmented $$DS_0$$ ), and $$DS_3$$ (augmented $$DS_1$$ ). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. …”
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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…Results: The architectures were assessed using (1) the performance metrics – accuracy, precision, recall, and F1-score, (2) statistical graphics to understand the prevalence of classifiers, Borda count voting method to identify the best CNN model, (3) Tukey's honestly significance difference test to identify best-performing architecture. …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability by Tianrui Chen, Limeng Zhang, Weiwei Guo, Zenghui Zhang, Mihai Datcu

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Enhancing Neural Network Training Through Neuroevolutionary Models: A Hybrid Approach to Classification Optimization by Hyasseliny A. Hurtado-Mora, Luis A. Herrera-Barajas, Luis J. González-del-Ángel, Roberto Pichardo-Ramírez, Alejandro H. García-Ruiz, Katea E. Lira-García

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Macroeconomic Determinants of Investment Decisions for Medium and Large Enterprises in Poland’s Manufacturing Sector by Adam Andrzej Zając, Michał Wielechowski, Krzysztof Smoleń, Dariusz Karaś

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Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling by Chaitanya B. Pande, Pranaya Diwate, Israel R. Orimoloye, Lariyah Mohd Sidek, Arun Pratap Mishra, Kanak N. Moharir, Subodh Chandra Pal, Fahad Alshehri, Abebe Debele Tolche

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Constructing A Knowledge-driven and Data-driven Hybrid Decision Model for Etiological Diagnosis of Ventricular Tachycardia by WANG Min, HU Zhao, XU Xiaowei, ZHENG Si, LI Jiao, YAO Yan

“…The precision, recall, and F1 scores of the three models were as follows: the knowledge-driven model achieved 80.4%, 79.1%, and 79.7%; the data-driven model achieved 88.4%, 88.5%, and 88.4%; and the hybrid model achieved 90.4%, 90.2%, and 90.3%.ConclusionsThe hybrid model integrating knowledge-driven and data-driven approaches demonstrated higher accuracy, and all its decision outcomes were based on evidence-based practices, aligning more closely with the actual diagnostic reasoning of clinicians. …”
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Immunohistochemical Breast Cancer Profiling Among Iraqi Women: Molecular Subtype Classification, Clinicopathology Associations, and Treatment‐Decision Making Tools: A Cross‐Section... by Alaa Salah Jumaah, Roaa Ali Shaker, Zainab Al‐Ali, Kaswer Musa Jaafar Altoriah, Aseel Al‐Quzweni, Salam Salah Jumaah, Akeel Abed Yasseen, Katherine Ann McAllister, Haider J. Al Shiblawi

“…The tumor profiles were modelled with treatment combinations using the PREDICT decision making tool of up to 10‐year OS outcomes; > 5% differences were deemed clinically relevant. …”
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Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods by Mürsel Kahveci

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Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees by Dorota Wilk-Kołodziejczyk, Aleksandra Nowotny, Izabela Krzak, Adam Tchórz, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka, Mirosław Głowacki, Marzanna Książek, Łukasz Marcjan

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Adaptive Token Mixer for Hyperspectral Image Classification by Shuhan Lei, Meng Zhang, Yuhang Wang, Nan Tang, Ni Jia, Lihua Fu

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Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model by Jia-Ming Zhu, Yu-Gan Geng, Wen-Bo Li, Xia Li, Qi-Zhi He

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An extension of the Spiegelhalter-Knill-Jones method for continuous covariates in clinical decision making by Bart K. M. Jacobs, Tafadzwa Maseko, Lutgarde Lynen, Aquiles Rodrigo Henriquez-Trujillo, Jozefien Buyze

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Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System by Najmeh Sadat Jaddi, Salwani Abdullah, Zalinda Othman, Yazrina Yahya, Mohd Zakree Ahmad Nazri, Say Leng Goh, Fatemeh Alvankarian, Jafar Alvankarian

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GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets by Ehsan Namjoo, Alison N. O’Connor, Jim Buckley, Conor Ryan

“…This model compression yields a transparent, IF–THEN rule-based decision process that remains faithful to the original high-performing model. …”
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OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging by Xiaolu Ren, Xiaolu Ren, Lingxuan Hou, Shan Liu, Peng Wu, Siming Liang, Haitian Fu, Chengquan Li, Ting Li, Yongjing Cheng

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ViT–KAN Synergistic Fusion: A Novel Framework for Parameter- Efficient Multi-Band PolSAR Land Cover Classification by Songli Han, Dawei Ren, Fan Gao, Jian Yang, Hui Ma

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