SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE
Keywords:Hormonal Imbalance, Soft Computing, Fuzzy Logic, Genetic Algorithm
Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.
 Helal, I. A., Mohammed, Z. S., Muhson, H. D., Ali, E. S., & Majeed, M. R. (2015). Association between female infertility and hormonal imbalance. College of Nursing, University of Kufa. DOI: 10.13140/RG.2.1.1222.5041.
 Bunzenmeyer, J. (2016). What does hormone imbalance mean and how can it affect me? Retrieved online from www.ndclinic.com.
 Kyle, C. A. (2008). A handbook for the interpretation of laboratory tests .4th. ed., diagnostic Medlab, pp: 345.
 Cooper T. G., Noonan E., von Eckardstein S., Auger J., Baker H.W.G., Behre H. M., Vogelsong K. M. (2010). World Health Organization reference values for human semen characteristics, Human Reproduction Update, Vol. 16, No. 3, pp. 231-245.
 Wolfe D. (2016). 10 Signs and Symptoms of Hormonal Imbalance. Retrieved online from www.docofdetox.com
 Naveed, S., Ghayas, S., Hameed, A. (2015). Hormonal imbalance and its causes in young females. Journal of Innovations in Pharmaceuticals and Biological Sciences (JIPBS), Vol. 2, No. 1, pp.12-16.
 Bigus C (2013). Nine Signs You Have a Hormonal Imbalance + Easy Ways to Fix it. Retrieved from www.mindbodygreen.com.
 Hills J. (2014). 13 Signs You Have a Hormonal Imbalance and What You Can Do About it. Healthy and Natural World. Retrieved from www.healthyandnaturalworld.com
 Adaikan, P. G. and B. Srilatha (2003) Oestrogen-mediated hormonal imbalance precipitates erectile dysfunction, International Journal of Impotence Research 15:38–43.
 MedicineNet (2019) Hormone imbalance, https://www.medicinenet.com/search/mni/hormonal%20imbalance Assessed 20-11-2019
 Tarantino G, Savastano S, Colao A. (2010) Hepatic steatosis, low-grade chronic inflammation and hormone/growth factor/adipokine imbalance. World J Gastroenterol; Vol. 16, No. 38, pp.4773-4783, Available at: http://www.wjgnet.com/1007-9327/full/v16/i38/4773.htm DOI: http://dx.doi.org/10.3748/wjg.v16.i38.4773.
 Singh, A., & Kaur, R. A (2015). Study of Hybrid Soft Computing Techniques. International Journal of Advance Foundation and Research in Science & Engineering (IJAFRSE).
 Parthiban, L., & Subramanian R. (2007). Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm. World Academy of Science, Engineering and Technology International Journal of Medical and Health Sciences Vol. 1, No. 5.
 Zadeh, L.A (1965) Fuzzy sets. Inform Control, Vol. 8, No. 3, pp.338–353.
 Ejodamen, P. U., & Imianvan, A. A. (2018). Genetic Neuro-Fuzzy System for the
Identification of Citrus Huanglongbing. 27th National Conference, Nigeria Computer
Society (NCS), University of Ibadan, Oyo State, Nigeria.
 Ekong, V. E., Inyang, U. G., & Onibere, E. A. (2012). Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid. Modern Applied Science; Vol. 6, No. 7, pp. 79-88.
 Vijaya, K., Nehemiah, H. K., Kannan, A., and Bhuvaneswari, N.G. (2010). Fuzzy Neuro Genetic Approach for Predicting the Risk of Cardiovascular Diseases. International Journal of Data Mining, Modeling and Management, Vol. 2, No. 4, pp. 388-402. Available at: http://dx.doi.org/10.1504/IJDMMM.2010.035565
 Uzoka F-M. E., Obot O., Barker K., & Osuji J. (2011). An experimental
comparison of fuzzy logic and analytic hierarchy process for medical
decision support systems. Computer Methods and Programs in Biomedicine
103 (2011) 10–27.
 Obot, O. U. and Uzoka, F-M. E. (2008). Fuzzy rule-based framework for the management of tropical diseases. Int. J. Medical Engineering and Informatics, Vol. 1, No. 1.
 Imianvan, A. A., & Obi, J. C. (2012). Decision Support System for the Identification of Tuberculosis using Neuro Fuzzy Logic. Nigerian Annals of Natural Sciences, Vol. 12, No. 1, pp.12– 20.
 Obi, J. C., Imianvan, A. A. & Ekong, V. E. (2012). Genetic Neuro-Fuzzy System for the Intelligent Recognition of Stroke, Journal of Computer Science and its applications, An International Journal of The Nigeria Computer Society, Vol. 19, No.1, pp. 24-31.