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A Classification Model Based on an Adaptive Neuro-fuzzy Inference System for Disease Prediction

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Bio-inspired Neurocomputing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 903))

Abstract

Disease prediction is now prevalent in the health industry due to the need to increase the life expectancy of any human being. Diseases are of different kinds like physical, mental, environmental, human-made. Recently, machine learning is paving its path toward perfection in the field of the health industry. Machine learning (ML) with the artificial neural network (ANN) is a useful tool for solving different aspects of a complex real-time situation analysis that includes both biomedical and healthcare applications. The system can help in eradicating problems faced by medical practitioners in delivering unbiased results. Patients suffering due to the unavailability of experienced as well as expensive medical help can be benefitted from this system. Machine learning has been recently one of the most active research areas with the development of computing environment in hardware and software in many application areas with highly complex computing problem definition. The medical care sector is one of them; it is capable of the automation process by saving time-consuming and subjective by nature. So, ML and ANN-based processes provide unbiased, repeatable results. The broader dimensionality nature of data in medicine reduces the sample of pathological cases made of advanced ML and ANN learning techniques to clinical interpretation and analysis. The medical understanding and disease detection mostly depend on the number of experts and their expertise in the area of the problem, which is not enough. The medical data analysis requires a human expert with the highest level of knowledge with a high degree of correctness. It is prone to error, ML, and the ANN learning method can improve the accuracy with the clinical standard for computer-based decision-making models and tools with expert behavior. The traditional methods like Bayesian network, Gaussian mixture model, hidden Markov model implemented for disease recognition on humans, animals, birds, etc., applied by many researchers have failed to reach the optimum accuracy and competence. Many intelligent systems introduced for the identification of diseases like probabilistic neural network, decision tree, linear discriminant analysis, and support vector machine. Machine learning-based adaptive neuro-fuzzy inference system for disease detection and recognition is the next step of evolution in an artificial neural network. In this chapter, the usefulness of machine learning along with ANFIS utility toward a medico issue in the healthcare sector is discussed.

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Author information

Authors and Affiliations

  1. Department Electronics and Communication, Birla Institute of Technology, Ranchi, India

    Ricky Mohanty & Sandeep Singh Solanki

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India

    Pradeep Kumar Mallick

  3. Department of Computer Science and Engineering, Orissa Engineering College, Bhubaneswar, India

    Subhendu Kumar Pani

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  1. Ricky Mohanty
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  2. Sandeep Singh Solanki
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  3. Pradeep Kumar Mallick
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  4. Subhendu Kumar Pani
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Corresponding authors

Correspondence to Pradeep Kumar Mallick or Subhendu Kumar Pani .

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Editors and Affiliations

  1. Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Akash Kumar Bhoi

  2. KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  3. National Taipei University of Technology, Taipei, Taiwan

    Chuan-Ming Liu

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

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Mohanty, R., Solanki, S.S., Mallick, P.K., Pani, S.K. (2021). A Classification Model Based on an Adaptive Neuro-fuzzy Inference System for Disease Prediction. In: Bhoi, A., Mallick, P., Liu, CM., Balas, V. (eds) Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-15-5495-7_7

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