Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination.

Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate.

Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%.

Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.