Abstract
Diabetes is one of the high-risk medical diseases, in which, blood sugar levels gets higher. It is one of the leading causes of increase in deaths, worldwide. In 2040, the world's diabetic patients will hit 642 million approximately, according to the rising morbidity in recent years. This leads to an observation that one of the ten adults in the future will suffer from diabetes. This motivates researchers to adapt both machine learning and deep learning for early diagnosis of diabetic patients. Significant number of data mining and machine learning techniques has applied on diabetes dataset for risk prediction of disease. The objective of this paper is to analyze all the famous machine learning techniques, namely, Random Forest, Decision Trees, K-Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Extra Trees on well-known diabetic patient's dataset PIMA. Thereafter, deep learning model ANN is also applied for comparative analysis. From the obtained results, it is observed that extra tree classifier outperforms other algorithms having an accuracy of 81.16% along with a good AUC score of 81%. In addition, artificial neural network (ANN) obtains an accuracy of 73.58% on test dataset, which is quite low as the dataset is small.
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Malviya, L., Mal, S., Lalwani, P., Chadha, J.S. (2021). Diabetes Classification Using Machine Learning and Deep Learning Models. In: Bajpai, M.K., Kumar Singh, K., Giakos, G. (eds) Machine Vision and Augmented Intelligence—Theory and Applications. Lecture Notes in Electrical Engineering, vol 796. Springer, Singapore. https://doi.org/10.1007/978-981-16-5078-9_40
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DOI: https://doi.org/10.1007/978-981-16-5078-9_40
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