Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir - Amir, Nur Hazirah (2019)
Mood is a psychological state that responsible for emotional feeling in human mind and it deeply affected by premenstrual symptoms (PMS). Large part of women community undergoes that phase. It can cause a sudden changes of mood which lead to decreasing of the Quality of Life (QOL). With this project creates a mobile app that analyse the mood pattern which also correspond with menstrual cycle to be able to predict mood. The development model used for this project is Agile model development cycle. It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. The template design was built using Node.js and Visual Code Studio. And it used Cordova for designing interface. The ambulatory assessment used was momentary self-report which conducted with Google Form. The project was tested to 30 women which was chosen selectively with diverse age of group which have a regular menstruation period. The accuracy for this project was determined within 8 weeks of data collected. The analysis for this shows that the dataset is too small to give strong validation of the its impact. Increasing the sample size and randomly choose the sample would improve this project as the prediction could be more accurate and reliable.