{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:32:56Z","timestamp":1777984376487,"version":"3.51.4"},"update-to":[{"DOI":"10.12688\/f1000research.169436.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000}}],"reference-count":45,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020612","name":"Kalinga Institute of Industrial Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020612","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>Background The use of ensemble learning has been crucial for improving predictive accuracy in healthcare, especially with regard to critical diagnostic and classification problems. Ensemble models combine the strengths of multiple ML models and reduce the risk of misclassification, which is important in healthcare, where accurate predictions impact patient outcomes. Methods This study introduces the Gradient-Based Weight Optimized Ensemble Model (GBWOEM), an advanced ensemble technique that optimizes the weights of five base models: Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and K-Nearest Neighbours (KNN), through optimizing the weights. Two variants, GBWOEM-R (random weight initialization) and GBWOEM-U (uniform weight initialization), were proposed and tested on five healthcare-related datasets: breast cancer, Pima Indians Diabetes Database, diabetic retinopathy debrecen, obesity level estimation based on physical condition and eating habits, and thyroid diseases. Results The test accuracy of the proposed models increased to 0.48-8.26% over the traditional ensemble models, such as Adaboost, Catboost, GradientBoost, LightGBM, and XGBoost. Performance metrics, including ROC-AUC analyses, confirmed the model\u2019s efficacy in handling imbalanced data, highlighting its potential for advancing predictive consistency in healthcare applications. Conclusion The GBWOEM model improves the predictive accuracy and offers a reliable solution for healthcare applications even when dealing with the imbalance data. This strategy has the potential to ensure patient outcomes and diagnostic consistency in healthcare settings.<\/ns3:p>","DOI":"10.12688\/f1000research.169436.2","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:39:06Z","timestamp":1769510346000},"page":"1161","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare"],"prefix":"10.12688","volume":"14","author":[{"given":"Surajit","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samaleswari P.","family":"Nayak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1355-3395","authenticated-orcid":false,"given":"Biswajit","family":"Sahoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4237-4591","authenticated-orcid":false,"given":"Satyananda","family":"Champati Rai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1808","DOI":"10.3390\/healthcare11121808","article-title":"Ensemble Learning for Disease Prediction: A Review.","volume":"11","author":"P Mahajan","year":"Jun. 2023","journal-title":"Healthcare."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1903-1912","DOI":"10.1109\/TCE.2023.3318850","article-title":"An Ensemble-Based Machine Learning-Envisioned Intrusion Detection in Industry 5.0-Driven Healthcare Applications.","volume":"70","author":"M Wazid","year":"Feb. 2024","journal-title":"IEEE Trans. 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The paper could benefit from a more detailed explanation of how the optimized weights of base models contribute to the final predictions. This would enhance the interpretability and practical relevance of the model, especially in healthcare applications.<\/b> \n<b>Response:<\/b> We thank the reviewer for this valuable suggestion regarding interpretability. To address this, we have expanded the result to explicitly detail how the optimized weights reflect the contribution of each base model. For example, as noted in the revised manuscript, in the GBWOEM-R variant, the Random Forest Classifier (RFC) was assigned a significantly higher weight (1.002) compared to Logistic Regression (0.058), quantitatively demonstrating RFC's stronger contribution to the decision boundary for this specific dataset. Additionally, as requested, we have updated the relevant performance comparison figures to include the final weights of Random Weight (RW) and Uniform Weight (UW) initialization strategies. This visual comparison highlights how our optimization method specifically identifies and prioritizes the most reliable classifiers for healthcare diagnostics, rather than relying on static or arbitrary averaging.  \n<b>Q2. The datasets used are reported to have class imbalance. The authors should clarify how this imbalance was addressed during training and evaluation to ensure the results are reliable.<\/b> \n<b>Response:<\/b> We appreciate the reviewer highlighting this important aspect of data validity. We have analysed the class distribution for all five datasets used in the study. Four of the datasets exhibit a ratio approximating 60:40, which falls within the range of practical balance where standard classifiers perform effectively without intervention. For the dataset exhibiting an imbalance, we empirically evaluated the model's performance without synthetic balancing (such as SMOTE). We have clarified in the Methodology section that a part of the dataset is kept as test data set to test the model performance in unseen data. We observed that the model converged successfully with a negligible gap between training and testing performance metrics (specifically, Recall and F1-score). This indicates that the feature space was sufficiently distinct to allow the model to learn the minority class boundaries effectively. We have clarified that we avoided synthetic oversampling for this specific case to prevent the introduction of noise and to ensure the evaluation reflects the model's robustness on naturally occurring data distributions.  \n<b>Q3. In Figure 9, ROC curves with AUC values near 1.00 may indicate potential overfitting. The authors should verify these results, possibly by evaluating the models on independent or cross-validation datasets to ensure generalizability.<\/b> \n<b>Response:<\/b> We agree that AUC values approaching 1.00 warrant careful scrutiny. We have re-verified the results presented in Figure 9. The high AUC values are attributed to the high separability of the datasets, which is consistent with existing literature on the respective datasets. Due to strict page length constraints in the submitted manuscript, we could not include the exhaustive Train-vs-Test comparison data for every model variant. However, to address the reviewer\u2019s concern, we have provided this detailed analysis below (see Table 1). &nbsp;As illustrated in Table 1, the performance metrics (especially F1-score, recal,l and precision) for both training and testing phases fall within a consistent range with minimal deviation. The absence of a significant drop in performance from Training to Testing confirms that the model generalizes well to unseen data and is not memorizing the training set. We have added a statement to the Results section clarifying that this stability across splits validates the high AUC scores. \n<b>Table 1:<\/b> Performance matrices of GBWOEM-R on Breast Cancer Dataset (up to 3 model pairs) https:\/\/docs.google.com\/spreadsheets\/d\/1BFQRoqDtJme5ZrmqXKYFwuNuKaGeyq7vQyu6L3k_PYo\/edit?usp=sharing","URL":"https:\/\/f1000research.com\/articles\/14-1161\/v2#referee-comment-15283","order":2,"name":"referee-comment-15283","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.195371.r458348, Anjum John, Pushpagiri Medical College Hospital, Thiruvalla, Kerala, 14 Feb 2026, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/14-1161\/v2#referee-response-458348","order":3,"name":"referee-response-458348","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The author(s) declared that no grants were involved in supporting this work","order":4,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}