An Improved Model For Baby Gender Guide Predictive System Using K-Nearest Neighbour Algorithm - Tenas God’swill, Ezikwa; Victoria Tenas, Ezikwa (International Journal of Computer Sciences and Engineering, 2022)
In almost every homes, having the desired gender of baby present could also foster the joy needed for the coexistence between couples in the family whereas in some instances, not having the desired gender of baby becomes the root cause of every other family problems. This research focuses on: “An Improved Model for Baby Gender Guide Predictive System using KNN classification algorithm”. The model uses the trained dataset for prediction directly. The predictions were made by going through the trained dataset to obtain a new instance (x) for nearest neighbors and displaying the result of K instances. The new system was designed using object oriented analysis and design.methodology and was implemented using Hypertext Preprocessor (PHP) programming language and MySQL as the database software. The result of the new system indicates that the accuracy of the gender of babies predicted prior-to and within the first trimester of conception had a higher degree of accuracy of 92% which is superior to the sonographic system with an accuracy of 54%.