Implementation of Neural Networks for Predicting Diabetes Using Keras
DOI:
https://doi.org/10.33998/jakakom.2026.6.1.2479Keywords:
Diabetes, Neural Network, Machine LearningAbstract
Diabetes is a disease with a rising prevalence worldwide. Early prediction of diabetes can enable timely
interventions and management, which in turn can help reduce the adverse impact of this disease. In this study, use employed a
Machine Learning approach using Neural Network to predict whether an individual will develop diabetes within the next 5
years. The prediction process involved preprocessing stages such as filtering, normalization, handling missing data, and
feature transformation if necessary. Subsequently, the dataset was divided into training and testing data. The training data was
used to train the Neural Network model, while the testing data was used to evaluate the model's performance. Based on our
study, after training the Neural Network model, we achieved an accuracy rate of 71%. The prediction was further tested on 10
sample data, where 7 out of 10 predictions matched the output in the dataset. These findings demonstrate the effectiveness of
the Neural Network method in predicting diabetes data. This result has the potential to enhance our understanding of the risk
factors contributing to the development of diabetes and contribute to the development of better predictive approaches in the
prevention and management of this disease. With accurate early prediction, timely interventions and management can be
implemented, thereby helping to mitigate the adverse impact of this disease.
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