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A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

Author

Listed:
  • Prableen Kaur

    (Department of Computer Science and Applications, DAV University, Jalandhar, India)

  • Manik Sharma

    (Department of Computer Science and Applications, DAV University, Jalandhar, India)

Abstract

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.

Suggested Citation

  • Prableen Kaur & Manik Sharma, 2017. "A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(2), pages 70-91, April.
    • Handle: RePEc:igg:jismd0:v:8:y:2017:i:2:p:70-91
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