BBT-based Ovulation/ Anovulation Classification Using Artificial Neural Network

Shia Chai Shin, Farhanahani Mahmud, Muhammad SyukriMohd Yazed, Marlia Morsin, NurAnida Jumadi, Aizan Masdar

Fertility awareness method (FAM) with a basal body temperature (BBT) measurement and charting technique has been commonly used to represent women’s menstrual cycle which is an important indicator of the women’s fertility health. However, a woman may face difficulties in understanding her menstrual cycle by comparing the plotted BBT chart with the ideal chart since there are different variations in the chart patterns. In order to increase the feasibility of understanding women’s menstrual cycle by using the BBT chart, a feed forward back propagation of an artificial neural network classification for fertility conditions of ovulation and anovulation based on BBT chart has been done in this project by using MATLAB R2016a software. The network had acquired a good performance of 84.8% accuracy in classifying the BBT chart to their respective fertility conditions, ovulation or anovulation.

Volume 12 | 02-Special Issue

Pages: 772-779

DOI: 10.5373/JARDCS/V12SP2/SP20201131