Abstract
Diabetes is reaching in widening proportions in many developing countries in the world due to a lack of health understanding and poor eating habits. Type-2 diabetes (as of late suggested as non-insulin-ward, or adult start) originates from the body’s inefficient usage of insulin. Looking over the statistics around 425 million adults, age between 20 and 79 years are suffering from type-2 diabetes; which has caused around 4 million deaths to date. Therefore, it is important to have a machine learning technique that can adequately pinpoint the tendency of type-2 diabetes in the heap of fragments quickly. AI has conjured a lot of enthusiasm among specialists. The proof introduced by a few reports recommends that AI approaches have the potential to yield higher precision in the order of information in contrast with different techniques. This paper reviews five different amalgam classification techniques for the prediction of type-2 diabetes. RF-WFS and XGBoost algorithm give the most elevated precision, affectability, and specificity of the model are 93.75%, 91.79%, and 94.8%, separately in the forecast of the disease.
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Abbreviations
- ADASYN:
-
Adaptive synthetic sampling method
- AI:
-
Artificial intelligence
- ANN:
-
Artificial neural network
- ASSM:
-
Adaptive synthetic sampling method
- BMI:
-
Body mass index
- DT:
-
Decision tress
- FN:
-
False negative
- FP:
-
False positive
- MLP:
-
Multi layer perceptron
- NB:
-
Naïve Bayes
- RF:
-
Random forest
- SVM:
-
Support vector machine
- TN:
-
True negative
- TP:
-
True positive
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Rajput, R., Lenka, R.K., Chacko, S.J., Javed, K.G., Upadhyay, A. (2021). Overview of Amalgam Models for Type-2 Diabetes Mellitus. In: Abraham, A., Castillo, O., Virmani, D. (eds) Proceedings of 3rd International Conference on Computing Informatics and Networks. Lecture Notes in Networks and Systems, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-9712-1_48
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DOI: https://doi.org/10.1007/978-981-15-9712-1_48
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