Abstract
Diabetes is a very common chronic disease that is of rising concern. According to the World Health Organization, it is estimated that approximately 422 million people worldwide suffer from diabetes. By 2040, the number of people suffering from diabetes is estimated to increase to approximately 642 million. Due to diabetes, one person dies every six seconds (five million a year) which is more than HIV, tuberculosis, and malaria combined, and 1.6 million deaths are due to diabetes every year. In the previous part, we have covered some of the traditional ways of diabetes prediction. The use of Machine Learning applications in this disease can reform the approach to its diagnosis and management. Support vector machines, logistics regression, K-Nearest Neighbor (KNN), and decision tree algorithms were used to identify the model. These techniques are more suitable to detect early signs of diabetes based on nine important parameters. Accuracy, F-Measure, Recall, Precision, and Receiver Operating Curve (ROC) measures are used to define the performance of the different machine learning techniques.
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References
Centers for Disease Control and Prevention. National diabetes statistics report, Centers for Disease Control and Prevention website (2017)
www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf, External link (PDF, 1.3 MB) Updated July 18, 2017. Accessed August 1, 2017
K. Patil, S.D. Sawarkar, S. Narwane, Designing a model to detect diabetes using machine learning, Int. J. Eng. Res. Technol. (IJERT) 8(11) (November 2019)
B. Yadav, S. Sharma, A. Kalra, Supervised Learning technique for prediction of diseases, in Intelligent Communication, Control and Devices (Springer, Singapore, 2018), pp. 357–369
A. Dagliati, S. Marini, L. Sacchi, G. Cogni, M. Teliti, V. Tibollo, ... R. Bellazzi, Machine learning methods to predict diabetes complications. J. Diabet. Sci. Technol. 12(2), 295–302 (2018)
I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, I. Chouvarda, Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104–116 (2017)
A. Choudhury, D. Gupta, A survey on medical diagnosis of diabetes using machine learning techniques, in Recent Developments in Machine Learning and Data Analytics (Springer, Singapore, 2019), pp. 67–78
J.A. Carter, C.S. Long, B.P. Smith, T.L. Smith, G.L. Donati, Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes. Expert Syst. Appl. 115, 245–255 (2019)
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Shahade, M., Awate, A., Nandwalkar, B., Kulkarni, M. (2023). Diabetes Disease Prediction Using KNN. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_24
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DOI: https://doi.org/10.1007/978-981-99-0550-8_24
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