DIABETIC RISK PREDICTION FOR WOMEN USING BOOTSTRAP AGGREGATION ON BACK-PROPAGATION NEURAL NETWORKS

The greatest challenge to current health care is the rapid growth of diabetes. This paper helps in predicting diabetes by using bootstrap aggregation with backpropagation neural network. Backpropagation is a method used in artificial neural network to calculate the error contribution of each neuron after a batch of data is processed. Bootstrap aggregation is an ensemble method which combines the predictions from multiple neural networks together to make more accurate predictions than any individual model. The dataset used is collected from UCI machine learning repository which contains information of persons with and without diabetics. Python scikit-learn library was used for designing the neural network and for implementing bootstrap aggregation. Results with greater accuracy have been obtained.