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Prediction Equations in Spirometry Using Demographic and Spirometric Values

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Intelligent Computing, Information and Control Systems (ICICCS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1039))

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Abstract

Major research in prediction equations for spirometry in adults take only demographic data i.e. gender, age, height and weight into its consideration. Furthermore, the studies conducted in India on the prediction equations for spirometry in adults are inadequate and old, to make their validity unreliable. We have developed prediction equations for spirometry in the adults of Pune in Maharashtra, India by considering their measured spirometric data in addition to the demographic data. For doing so, a dataset composed of 2092 healthy subjects from Pune, who underwent spirometry tests and whose results were recorded in the database was used. Linear, Quadratic and Logarithmic prediction equations were developed using the dataset for forced vital capacity, and 13 other parameters, and the accuracy was compared with one another as well as the existent equations. The results have denoted a significant increase in accuracy when compared to previous studies. The correlation between spirometric parameters is convincing to take into account for developing more specific & accurate predictions.

A. Patil, S. Hassan, T. Nayak, V. Attar, G. Sakhare and S. Joshi—Equal contribution.

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Notes

  1. 1.

    FEV1 - Forced Expiratory Volume in the first second.

  2. 2.

    FEV3 - Forced Expiratory Volume in the third second.

  3. 3.

    FVC - Forced Vital Capacity.

  4. 4.

    FEV1/FVC - Ratio of FEV1 and FVC.

  5. 5.

    PEF - Peak Expiratory Flow.

  6. 6.

    FEF25 - Forced Expiratory Flow with 25% interval.

  7. 7.

    FEF50 - Forced Expiratory Flow with 50% interval.

  8. 8.

    FEF75 - Forced Expiratory Flow with 75% interval.

  9. 9.

    FEF2575 - Mean Forced Expiratory Flow in 25–75%.

  10. 10.

    FIVC - Forced Inspiratory Vital Capacity.

  11. 11.

    PIF - Peak Inspiratory Flow rate.

  12. 12.

    FIF25 - Forced Inspiratory Flow with 25% interval.

  13. 13.

    FIF50 - Forced Inspiratory Flow with 50% interval.

  14. 14.

    FIF75 - Forced Inspiratory Flow with 75% interval.

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Acknowledgement

We would like to thank BRIOTA Technologies Private Limited for their support and taking ownership of the research work presented in paper.

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, College of Engineering, Pune, India

    Akash Patil, Safna Hassan, Tejas Nayak & Vahida Attar

  2. Research and Development Department, BRIOTA Technologies Private Limited, Shivajinagar, Pune, India

    Gajanan Sakhare & Shardul Joshi

Authors
  1. Akash Patil
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  2. Safna Hassan
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  3. Tejas Nayak
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  4. Vahida Attar
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  5. Gajanan Sakhare
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  6. Shardul Joshi
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Corresponding author

Correspondence to Akash Patil .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Vaigai College of Engineering, Madurai, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Electrical and Computer Engineering, University of Applied Sciences, Egaleo, Attiki, Greece

    Klimis Ntalianis

  3. Department of Business Administration, The Gerald Schwartz School of Business, Antigonish, NS, Canada

    Ram Palanisamy

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Patil, A., Hassan, S., Nayak, T., Attar, V., Sakhare, G., Joshi, S. (2020). Prediction Equations in Spirometry Using Demographic and Spirometric Values. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_51

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