Diabetes prediction using machine learning / Muhammad Adib Mohd Nazri and Mahfudzah Othman - Mohd Nazri, Muhammad Adib; Othman, Mahfudzah (College of Computing, Informatics and Media, UiTM Perlis, 2023)
The increasing population has led to longer wait times for patients in the medical industry, particularly for diabetes check-ups. Machine learning technology can assist with speeding up the process of identifying diabetes by utilizing algorithms and techniques to train on previous data and predict potential problems. Two types of machine learning, supervised and unsupervised, are used to assist patients and the medical sector. The data and results from these methods can be used as references for diagnosis. Based on the diagnostic measurement data gathered for this study, it is found that using prediction model can assist patients and the medical sector in predicting diabetes The effectiveness of these methods will be determined by evaluating their accuracy using various metrics after testing and training. An algorithm or method with a high percentage of accuracy will be considered effective when compared to others. In summary, machine learning technology can help improve the efficiency of identifying diabetes by analyzing previous data and making predictions, which can ultimately benefit both patients and the medical industry.