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Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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A Correction to this article was published on 07 May 2024

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Abstract

In the medical field, ankylosing spondylitis (AS) is arthritis with symptoms that differs from person to person and takes a long time to evaluate. For predicting radiographic progression, the prediction within the prognostics employing the approach of time-series records performed reasonably well when used with clinical variables from the first visit dataset. The integration and analysis of numerous variables of different types had limitations as per prior research work under statistical analysis on the radiographic progressions. With the time-series approach propagated through the records fed via electronic means, the study has been developed utilizing machine learning models (ML) for radiographic progression estimation among patients impacted towards AS. These models' performance might be enhanced by adding more data, including radiography of the spinal column or even the lifetime data. Comparison has been made within Model A/diagnostic B's precision through the development of clinical model gaining the reach of 2.5% subjected to spondylarthritis characteristics listed in the categorization criteria attributed to Spondylarthritis Assessment under International Society. Furthermore, the abridged model with linear regression gained a reach of 2.6% for viability with a lower range Model of A/B. Therefore, Model A/B has achieved superior development clinically within the model for the prediction of prognostics of patients affected by AS; its use may help with the early detection and diagnosis of AS.

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Authors and Affiliations

  1. Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522302, India

    Shaik Hasane Ahammad

  2. Dept of Data Science and Buisness Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, India

    R. Jayaraj

  3. Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamilnadu, India

    S. Shibu

  4. Department of Master of Computer Applications, S.A. Engineering College, Chennai, Tamilnadu, India

    V. Sujatha

  5. Department of Data Science, School of Computing, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, India

    Ch Prathima

  6. Deptartment of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

    L. Megalan Leo

  7. Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamilnadu, India

    R. Thandaiah Prabu

  8. Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh

    Md.Amzad Hossain

  9. Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32951, Egypt

    Ahmed Nabih Zaki Rashed

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  1. Shaik Hasane Ahammad
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Authors contributed equally in this study.

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Correspondence to R. Thandaiah Prabu, Md.Amzad Hossain or Ahmed Nabih Zaki Rashed.

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Key points

- This study has been developed utilizing machine learning models (ML) for radiographic progression.

- These models' performance might be enhanced by adding more data, including radiography of the spinal column or even the lifetime data.

- A bridged model with linear regression gained a reach of 2.6% for viability with a lower range Model of A/B.

- Model A/B has achieved superior development clinically within the model for the prediction of prognostics of patients.

The original online version of this article was revised: The original article contains uncorrected figures.

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Ahammad, S.H., Jayaraj, R., Shibu, S. et al. Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18236-6

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  • DOI: https://doi.org/10.1007/s11042-024-18236-6

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