Impacts of data anonymization on model prediction for diabetes. A feed-forward neural network is trained on the anonymized data and the accuracy is compared.
Two techniques are applied for anonymization: Laplace noise and generalization herarchies.
The dataset contains 16 features and 520 records which were collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladeshusi.
Islam M.M.F., Ferdousi R., Rahman S., Bushra H.Y. (2020) Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques. In: Gupta M., Konar D., Bhattacharyya S., Biswas S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_12
The complete analysis is done on Towards data science .