Gambhir Shalini, Malik Sanjay Kumar, Kumar Yugal
Department of Computer Science and Engineering, SRM University, Delhi NCR, Sonipat, Haryana, India.
Department of Information Technology, KIET Group of Institution, Ghaziabad, India.
J Med Syst. 2016 Dec;40(12):287. doi: 10.1007/s10916-016-0651-x. Epub 2016 Oct 29.
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.
在当今时代，软计算方法在解决各类问题中发挥着至关重要的作用，并提供了颇具前景的解决方案。由于软计算方法广受欢迎，这些方法也已应用于医疗保健数据，以便与传统方法相比能更有效地诊断疾病并获得更好的结果。软计算方法有能力根据问题领域进行自我调整。另一个方面是在探索和利用过程之间取得良好平衡。这些方面使软计算方法更强大、可靠且高效。上述特性使软计算方法更适合且有能力处理医疗保健数据。这篇综述论文的首要目标是识别用于诊断和预测疾病的各种软计算方法。第二个目标是识别应用这些方法的各种疾病。第三个目标是对临床支持系统的软计算方法进行分类。在文献中发现，大量软计算方法已被应用于从医疗保健数据中有效诊断和预测疾病。其中一些方法包括粒子群优化、遗传算法、人工神经网络、支持向量机等。在文献部分对这些方法进行了详细讨论。这项工作总结了过去十年中医疗保健领域使用的各种软计算方法。这些方法根据方法论分为五个不同类别，即基于分类模型的系统、专家系统、模糊和神经模糊系统、基于规则的系统以及基于案例的系统。在上述类别中讨论了许多技术，并且所有讨论的技术也以表格形式进行了总结。这项工作还关注软计算技术的准确率，并为每个类别提供表格信息，包括作者详细信息、技术、疾病以及效用/准确率。
