An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators
Victor Chang, Meghana Ashok Ganatra, Karl Hall, Lewis Golightly, Qianwen Xu
Abstract
Breakthroughs in healthcare analytics can help both the doctor and the patient. Analytics in healthcare can help spot and diagnose diseases early on. Therefore, they can also be used to improve healthcare quality and patient outcomes. Machine learning models can be used to find patterns in data and generate predictions based on these patterns. They are employed in healthcare applications for disease diagnosis, prognosis, and treatment. With the development of new algorithms and other technological innovations, these models have become more effective than ever at delivering patient treatment. The primary objective of this research is to apply different machine learning algorithms to predict the diagnosis of diabetes. Furthermore, these models are compared to determine the most effective model in this regard by evaluating their accuracy of prediction, alongside other performance metrics such as precision, recall and F1 score. Of the models investigated, Random Forest significantly outperformed the others, achieving an accuracy of 82.26%.