Abstract
Diabetes is a condition in which blood glucose, called as blood sugar, is high in an abnormal way. If the prediction of disease is possible at an early stage, then the risk factors associated with diabetes can be considerably lower in severity. The main problem and highly challenging task are to predict diabetes accurately, and the reason of this challenge is the diabetes dataset’s insufficient number of labels data and the existence of outliers. This research paper proposes a strong framework to predict the disease with the help of different types of machine learning (ML) algorithms: K-nearest neighbor (KNN), support vector machine (SVM), decision trees (DTs), Naive Bayes (NB), and logistic regression (LR). For implementation, a dataset has been taken from a PIMA database consisting patient’s health record, and these five machine learning techniques are applied to that dataset. A comparison between all the algorithms is presented in this paper. The motive of the paper is to provide assistance to doctors with their practitioners for the early prediction of diabetes using ML algorithms.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Goyal, S., Batra, N., Chhabra, K. (2023). Diabetes Disease Diagnosis Using Machine Learning Approach. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-19-2821-5_19
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DOI: https://doi.org/10.1007/978-981-19-2821-5_19
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