Utilizing a Hybrid LSTM with Adamax Optimization for Accurate Forecast and Surveillance of Breast Cancer Recurrence in Survivors

Rehabilitation challenges which breast cancer survivors experience include the following ones. The survivors comprise a range of lingering recurring which compromises its quality, fear of recurrence of life. In this study, we will try reducing this uncertainty by proposing an enhanced risk profiling model to evaluate chances of breast cancer recurrence, which will help each patient supervision and appropriate interferences. Leveraging clinical and control information, to the object of analysis based on demographic data, we used scholarly learning, collaborative schemes, and deep learning models. Various models in our proposed Hybrid Long Short-Term Memory (HLSTM) Network are created using Conv1D layers, LSTM, attention mechanism, and adamax optimizer. The model effectively introduces both local and the algorithm explored appreciable indirect interactions as well as sequential dependencies, thereby improving its forecasting abilities with 96% accuracy. Using all the evaluation parameters such as accuracy, precision, recall, and the F1 score measurements which proves our model outperforms possibilities in giving correct and credible prognosis. This research paves the way for a robust, user-friendly application designed for healthcare professionals to improve personalized care, empowering breast cancer survivors with knowledge and proactive health management.