An Enhanced Deep Learning Techniques for Precise Breast Cancer Forecasting via Clinical Data Analysis

Abstract

Cancer is still an acute issue because of the variability in the characteristics of its diagnosis and possibilities of false negative results in clinical examination. Single architecture deep learning models and machine learning models do lead to their inability to generalize successfully with dissimilar datasets, thus limiting the diagnostic validity of these models. To remove these obstacles, the proposed work proposes a hybrid prediction model, which is founded on optimized machine learning models and improved feature preprocessing techniques to obtain improved classification scores. The data set consists of 569 cases of breast cancer diagnosed, which can be downloaded freely, with 33 features. the results of the experiment indicate that the suggested approach has a maximum precision of 97.36, $(\mathrm{F} 1$-score $)=\text{0. 9 7},($ AUC value )= 0.97, which is indicative of a high level of discriminative ability. By a comparative analysis it was noted that ensemble-based models are capable of being used in the manipulation of structured medical data than traditional classifiers. The proposed structure proposes a computationally and medically relevant solution, which increases the precision of medical diagnosis and reduces the number of misclassifications, and, therefore, forms the basis of the timely diagnosis and choice in the medical systems.