A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

Abstract

The mortality rate of cancer is among the highest in the world. One death occurs every six in the world. Both machine learning (ML) and deep learning (DL) have been used by scientists to predict cancer. In addition, DL can analyze a huge amount of healthcare data in a short period of time to study the chances of recurrence, progression and patient survival. An accurate and quick framework for improving cancer prognosis prediction is presented in this study. A fast and accurate optimizer is necessary to predict both critical and non-critical cases, so a modified binary version of the Whale Optimization Algorithm (WOA) is proposed. Based on sigmoid transfer functions, this version identifies the subset of features that is minimally optimal while maximizing classification accuracy. This framework is composed of an optimized parameter Long-Short Term Memory (LSTM) Neural Network, with the input being the optimal set of feature selection layer. The proposed framework performs better than previous frameworks having an average accuracy of 100% and an execution time of 4113 seconds.

Keywords

• Binary modified WOA S-shaped (BMWOA-S)
• cancer diagnosis
• exploration
• exploitation
• LSTM
• feature selection