Abstract
Diagnosis and Prediction of Diabetes, a general chronic disease as well as a major threat to public health, can lead to improved treatment at its early stage. Classification techniques are widely used for the same. While many of the researchers have developed techniques using Machine Learning (ML) and Data Mining (DM) for the prediction of chronic diseases like diabetes, heart diseases, and cancers etc. considering the existing datasets as well as personally collected datasets, but still more research is continuing in this regard. In this paper, an Integrated Approach for Diabetes Prediction (IADP) has been introduced for diabetes prediction based on Hierarchical Agglomerative Clustering (HAC), Linear Discriminant Analysis (LDA) and Random Forests (RF) classifier. Some experiments are performed using Pima Indian Diabetes Dataset (PIDD) sourced from the UCI-ML repository with Python language concluding that the proposed approach provides better results in comparison with other conventional classification models. The proposed integrated approach will help out doctors to diagnose patients with diabetes professionally. Furthermore, it may be useful for investigations and predictions using different datasets, in different fields also, and resulting in valuable knowledge.
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Pati, A., Parhi, M., Pattanayak, B.K. (2022). IADP: An Integrated Approach for Diabetes Prediction Using Classification Techniques. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_28
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DOI: https://doi.org/10.1007/978-981-16-4807-6_28
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