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Existing approaches often rely on data augmentation techniques such as SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, but these methods can introduce noise, distort feature distributions, and reduce model interpretability. To overcome these challenges, we propose two augmentation-free neural network models, Double Conglomerate (D-CongNet) and Triple Conglomerate (T-CongNet), which integrate Polynomial feature transformations and SHAP (Shapley Additive Explanations) for feature analysis, ensuring both high predictive performance and robust interpretability. We evaluate our models on two publicly available datasets: the UCI Heart Disease dataset for CVD prediction and the Wisconsin Diagnostic Breast Cancer (WDBC) dataset for BC classification. D-CongNet and T-CongNet achieve state-of-the-art performance without augmentation, with 86.96% accuracy, 88.79% sensitivity, and 84.42% specificity for CVD, and 97.37% accuracy, 97.67% sensitivity, and 97.18% specificity for BC. Our models also provide clinically meaningful explanations, identifying MaxHR-ST_Slope as a critical predictor for CVD and concave points_mean-area_worst for BC, aligning with established medical knowledge. By eliminating the need for augmentation, D-CongNet and T-CongNet offer a transparent and reliable alternative to traditional oversampling methods, ensuring robust decision-making in medical applications. Our results demonstrate that augmentation-free ML models can achieve both high accuracy and interpretability, making them valuable tools for healthcare professionals seeking explainable AI-driven diagnostics.<\/jats:p>","DOI":"10.1186\/s40537-025-01152-3","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T14:53:11Z","timestamp":1744987991000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in cardiovascular and breast cancer diagnosis"],"prefix":"10.1186","volume":"12","author":[{"given":"Chukwuebuka Joseph","family":"Ejiyi","sequence":"first","affiliation":[]},{"given":"Dongsheng","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Francis Ofoma","family":"Eze","sequence":"additional","affiliation":[]},{"given":"Makuachukwu Bennedith","family":"Ejiyi","sequence":"additional","affiliation":[]},{"given":"Jennifer Ene","family":"Idoko","sequence":"additional","affiliation":[]},{"given":"Sarpong Kwadwo","family":"Asere","sequence":"additional","affiliation":[]},{"given":"Thomas Ugochukwu","family":"Ejiyi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"1152_CR1","doi-asserted-by":"publisher","unstructured":"Ejiyi CJ, Qin Z, Nneji GU, Monday HN, Agbesi VK, Ejiyi MB, Ejiyi TU, Bamisile OO. 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