2Department of Computer Science, Christ University, Ghaziabad-India
3Department of Electronics & Telecommunication, J D College of Engineering & Management, Nagpur-India
4Department of Economics and Finance, Kalinga Institute of Industrial Technology, Bhubaneswar-India
5Singapore Institute of Technology Engineering Cluster, Singapore-Singapore
6Dev Bhoomi Uttarakhand University, Dehradun-India
7Department of Information Technology, Marwadi University, Rajkot-India DOI : 10.5505/tjo.2026.4798 OBJECTIVE
The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer.
METHODS
The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant
tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations
with stacked classification, ensemble-based feature selection, and stacked classification. Performance
evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the
curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and
the comparison with baseline models were determined with the help of non-parametric tests (p<0.05).
RESULTS
The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity
of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between
folds was low, and statistically significant enhancement as compared to baseline classifiers were present.
CONCLUSION
The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately
remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed
framework as a proof-of-concept decision-support model for early breast cancer detection, with
potential translational relevance subject to further external clinical validation.