A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

GeneticAlgorithms(GA),AntColonyOptimization(ACO),ParticleSwarmOptimization(PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. Theseapproacheshavebeenusedinearlyprophecyofvariousdiseases.Thisarticleanalyzesthe efficacyofvariousNICtechniquesindiagnosingdiversecriticalhumandisorders.Itisobserved thatGA,ACO,PSOandABChavebeensuccessfullyusedinearlydiagnosisofdifferentdiseases. Ascompared toACO,PSOandABCalgorithms,GAhasbeenextensivelyused indiagnosisof ecology,cardiologyandendocrinologist.Inaddition,fromthelastsixyearsofresearch,ithasbeen observedthattheaccuracyaccomplishedusingGA,ACO,PSOandABCintheearlydiagnosisof cancer,diabetesandcardioproblemsliesbetween73.5%-99.7%,70%-99.2%,80%-98%and76.4% to99.98%respectively.Furthermore,ACO,PSOandABCarefoundtobebestsuitedindiagnosing lung,prostateandbreastcancerrespectively.Moreover,thehybriduseofNICtechniquesproduces betterresultsascomparedtotheirindividualuse. KeywoRDS Ant Colony Optimization, Artificial Bee Colony, Cancer, Diabetes, Disease Diagnosis, Genetic Algorithm, Heart Disease, Nature Inspired Techniques, Particle Swarm Optimization

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