A Selected Deep Learning Cancer Prediction Framework

Nadia G Elseddeq,Mofreh M Salem,Ali I Eldesouky,Sally M Elghamrawy

doi:10.1109/access.2021.3124889

Abstract

Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such as machine learning and DL to predict cancer prognosis, in addition to the probability of its recurrence, progression, and the patients’ survival estimation. Currently, all stakeholders are interested in the accuracy of cancer prognosis prediction. This study selected a framework within high accuracy and short prediction time from three DL frameworks for improving the performance of cancer prognosis prediction. This prediction requires a quick and high-accuracy optimizer, so we propose a binary version of the continuous AC-parametric whale optimization algorithm. This version is built on S-shaped transfer functions to identify the minimal optimal subset of features and maximize the classification accuracy. These frameworks proposed have the following forms: the first is a Feed-Forward Neural Network (FFNN) in which the input is the optimal set of feature selection. The second is an optimized parameter FFNN. The third is composed of a feature selection layer in which the best subset of selected features is for use as inputs in the optimized FFNN. We compared these frameworks using a comparative study. Our results show that, under all conditions, the third framework is superior to the others with an average accuracy of 100%, whereas the first and second frameworks achieved 94.97% and 93.12% accuracy, respectively.

Highlights

Cancer is the second disease that causes one out of six deaths worldwide [1]
The first experiment was to test the performance of the proposed optimizer, whereas the rest was to test the performance of the three frameworks
We proposed a selected Deep learning (DL) cancer prediction framework based on the dynamic group to achieve a balance between exploration and exploitation

Summary

Introduction

Cancer is the second disease that causes one out of six deaths worldwide [1]. In 2020, the International Agency for Research on Cancer predicted 19,300,000 new cases and 10,000,000 deaths [2]. All stakeholders, including patients, their caregivers, and providers, are interested in the accuracy of cancer prognosis prediction. One of the factors that contribute to the effective treatment of patients is prediction accuracy [3]. Disease detection involves the classification of tumor types and identification of cancer symptoms to train a machine that can identify new metastatic tumor types or diagnose a disease at an early stage because treatment in later stages is more difficult. Due to the enormous number of gene expression levels in a person, diagnosing cancer might be challenging. The basic difficulties linked to the treatment and prevention of illnesses are recognized to be inscribed by gene expression levels [4]

Methods

Results

Conclusion