A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

Int. J. Inf. Syst. Model. Des. Pub Date : 2017-04-01 DOI:10.4018/IJISMD.2017040105

Prableen Kaur, Manik Sharma

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引用次数: 29

Abstract

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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自然启发计算在致命疾病诊断中的应用综述

GeneticAlgorithms(GA)，AntColonyOptimization(ACO)，ParticleSwarmOptimization(PSO)和人工蜜蜂殖民地(ABC)是一些重要的自然启发的计算技术。Theseapproacheshavebeenusedinearlyprophecyofvariousdiseases。Thisarticleanalyzesthe efficacyofvariousNICtechniquesindiagnosingdiversecriticalhumandisorders。Itisobserved thatGA，ACO，PSOandABChavebeensuccessfullyusedinearlydiagnosisofdifferentdiseases。Ascompared toACO，PSOandABCalgorithms，GAhasbeenextensivelyused indiagnosisof生态，cardiologyandendocrinologist。Inaddition，fromthelastsixyearsofresearch，ithasbeen observedthattheaccuracyaccomplishedusingGA，ACO，PSOandABCintheearlydiagnosisof癌症，diabetesandcardioproblemsliesbetween73.5%-99.7%，70%-99.2%，80%-98%and76.4% to99.98%respectively。Furthermore，ACO，PSOandABCarefoundtobebestsuitedindiagnosing肺，prostateandbreastcancerrespectively。Moreover，thehybriduseofNICtechniquesproduces betterresultsascomparedtotheirindividualuse。关键词:蚁群优化，人工蜂群，癌症，糖尿病，疾病诊断，遗传算法，心脏病，自然启发技术，粒子群优化

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Int. J. Inf. Syst. Model. Des.

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