17Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges

S. Subasree1* and N. K. Sakthivel2

1Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

2Nehru Institute of Technology, Coimbatore, Tamil Nadu, India


In this chapter, we will discuss various opportunities and challenges and suggest a few intelligent architectures for better Diseases Pattern Predictions including Cancer Pattern Predictions that will improve Healthcare and ease medical practitioners and oncologist to make wise decision that will help patients for quality life.

In this chapter, we will discuss a few Genome Pattern Prediction Techniques and Tools of Machine Learning as Deep Learning Framework. A few previous works were proposed to address the abovementioned challenges. This chapter describes, analyzes, and makes the comparative study of the below mentioned Deep Learning Techniques and Schemes. These are i. Hierarchical Random Forest–based Clustering (HRF-Cluster), ii. Genetic Algorithm–Gene Association Classifier (GA-GA), iii. Weighted Common Neighbor Classifier (wCN), iv. Hybrid Ant Bee Algorithm (HABA), v. Multiobjective Particle Swarm Optimization (MPSO), vi. Kernelized Fuzzy Rough Set–Based Semi Supervised Support Vector Machine (KFRS-S3VM), vii. Enhanced Cancer Association–based Gene Selection Technique (ECAGS), viii. Enhanced Multi-Objective PSwarm (EMOPS), ix. Deep Learning–based Intelligent Human ...

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