Algorithm: Fuzzification of Anti-Mullerian Hormone and Thyroid

Author: Nutan Verma, Vivek Raich and Sharad Gangele*

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Abstract

Presented research work introduces a new fuzzy equation based mathematical methodology using fuzzy logics, fuzzified Anti-Mullerian Hormone (AMH) and Thyroid (TSH), predicts AMH and TSH hormonal profile. Predictions like what is its current status either it is Low, Normal or High. This technique developed using trapezoidal membership function and if-then rule. There are three trapezoidal membership functions μ_L, μ_N and μ_H as Low, Normal and High respectively. Traditionally individual medical expert prediction against hormone variation may or may not vary because these estimations are based on individuals experience. Proposed methodology will give a common prediction more accurately and easily.

Keywords

Infertility; AMH- Anti-Mullerian Hormone; TSH-Thyroid; μ_L is low fuzzy membership function; μ_N is normal fuzzy membership function; μ_H is high fuzzy membership function; Menstrual Cycle.

Conclusion

This investigation presents an innovative methodology to predict hormonal profile of AMH and TSH like what is its current status either it is Low, Normal or High. This technique fuzzify the normal reference range and gives a common computerized prediction against normal reference range which improve analysis and this will give more accuracy to the female infertility diagnosis and management by avoiding variation in the medical expert’s prediction contrary to the same medical situation. Presented fuzzification technique executed with trapezoidal membership function µL, µN and µH based on if-then rule restricted for healthy, adult and non-pregnant female. An algorithm is also designed to give better and easy understanding to the methodology.

References

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How to cite this article

Nutan Verma, Vivek Raich and Sharad Gangele (2018). Algorithm: Fuzzification of Anti-Mullerian Hormone and Thyroid Biological Forum – An International Journal 10(1): 125-128.