Advancements in Cancer Disease Prediction Using Modified Multi Model Deep Neural Networks

Cancer disease prediction is a critical task in healthcare that can significantly impact patient outcomes. This abstract presents a multimodal deep neural network (DNN) approach for cancer disease prediction, implemented in MATLAB. The proposed model leverages the power of DNNs to handle diverse types of data, such as medical images, genomic profiles, clinical records, and patient demographics. By integrating these multimodal data sources, the model aims to capture a comprehensive view of the disease, incorporating both genetic and phenotypic information. The MATLAB implementation allows for efficient data processing, model training, and evaluation. The performance of the proposed multimodal DNN model is assessed using benchmark datasets, and the results demonstrate its potential in accurate cancer disease prediction. Further research is needed to explore the interpretability of the model's predictions and expand its application to real-world clinical settings. Overall, this work contributes to advancing the field of cancer diagnostics by utilizing a multimodal DNN approach implemented in MATLAB for improved disease prediction.