Neural Network Model for Predicting Progression of Disease
This study investigates the use of neural network and their ability to predict disease progression based on clinical data and biomarkers. Using deep neural networks, a model was developed that efficiently analyzes the complex relationship between various factors and predict the probability of disease. The model was validated using retrospective analysis which indicated a good predictive ability that could be further utilized in better diagnostics and personalized treatment methods. More importantly, reserch detected specific pattern in the data, which enabled a more accurate prediction of disease at different stages. The study tried to improve a model by fine-tuned neural networks and tested other frameworks to gain the highets precision. This research also provides a basic for future work in directing the development of personalized therapeutic approaches based on individual patient characteristics.