An intelligent system framework for cancer prediction

The human body is constrained by the resistant framework, however now and again this safe framework alone isn’t fit to keep the human body from illnesses. Because they have the capacity to quickly predict outcomes from a huge amount of healthcare data, deep learning algorithms are essential for forecasting a variety of diseases. One of these illnesses is cancer, which accounts for one in every six fatalities globally. Many researchers now use predictive frameworks on intelligent Systems like machine learning and deep learning to estimate patient survival as well as the likelihood of cancer progression and recurrence. For enhancing the performance of cancer prognosis, this study chose one of three DL frameworks with high forecast accuracy and speed. These suggested frameworks come in the following varieties: First thing is the Feed Forward Neural Networks with the best selection of features set as input which is getting from the Support Vector Machine Algorithm. The next thing is an improved parameter FFNN. Specialists were directed on different Machine learning models on calculations and among those are Intelligent Systems, Backing, K-Closest Neighbor, Convolution Neural Network, and Naïve Bayes. The findings demonstrate that this framework will achieve better results respectively.