A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

Prableen Kaur (Department of Computer Science and Applications, DAV University, Jalandhar, India) and Manik Sharma (Department of Computer Science and Applications, DAV University, Jalandhar, India)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/IJISMD.2017040105
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Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. These approaches have been used in early prophecy of various diseases. This article analyzes the efficacy of various NIC techniques in diagnosing diverse critical human disorders. It is observed that GA, ACO, PSO and ABC have been successfully used in early diagnosis of different diseases. As compared to ACO, PSO and ABC algorithms, GA has been extensively used in diagnosis of ecology, cardiology and endocrinologist. In addition, from the last six years of research, it has been observed that the accuracy accomplished using GA, ACO, PSO and ABC in the early diagnosis of cancer, diabetes and cardio problems lies between 73.5%-99.7%, 70%-99.2%, 80%-98% and 76.4% to 99.98% respectively. Furthermore, ACO, PSO and ABC are found to be best suited in diagnosing lung, prostate and breast cancer respectively. Moreover, the hybrid use of NIC techniques produces better results as compared to their individual use.
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1. Introduction

IT has amazingly exaggerated the healthcare industry. No doubt, medical informatics is changing a scenario of medical industry. With the advancements, one is able to diagnose and cure the problems effectively. The latest vaccinations and other treatment methodology have exterminated lots of fatal diseases. However, there are still number of chronic diseases (Cardiovascular, Respiratory, Malignant, Digestive, Diarrheal, Depression, Malaria, Cancer, Diabetes etc.) that don’t appear to firmly vanish. The critical human disorders can be effectively handled if they are diagnosed properly.

NIC (Nature Inspired Computing) are inspired by the different aspects, components and species of nature viz. buildings, plants, insects, flowers, water fall, animal etc. Some of the important NIC techniques that have taken ideas from Physics, Biology, Genetics, and Chemistry are (Siddique & Adeli, 2015):

  • Genetic Algorithms (GA)

  • Swarm Intelligence (SI)

  • Particle Swarm Optimization (PSO)

  • Ant Colony Optimization (ACO)

  • Artificial Bee Colony (ABC)

  • Bat Algorithm (BA)

  • Cuckoo algorithm (CA)

  • Honey Bee Algorithm (HBA)

  • Water Wave Algorithm (WWA)

  • Ray Optimization (RO)

  • Fish Swarm Algorithm (FSA) etc.

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