title: Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.
abstract: In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.
authors: Department of Computer Science and Engineering, SRM University, Delhi NCR, Sonipat, Haryana, India.
authors: Shalini
authors: S
authors: Gambhir
authors: Department of Computer Science and Engineering, SRM University, Delhi NCR, Sonipat, Haryana, India.
authors: Sanjay Kumar
authors: SK
authors: Malik
authors: Department of Information Technology, KIET Group of Institution, Ghaziabad, India. yugalkumar.14@gmail.com.
authors: Yugal
authors: Y
authors: Kumar
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining; D020000:Decision Support Systems, Clinical; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D060388:Support Vector Machine
keywords: Artificial intelligence; Healthcare database; Medical data mining; Medical diagnosis; Soft computing