Prevention of Data Poisonous Threats on Machine Learning Models in e-Health

Machine learning is widely used across various fields, including e-health, to enhance efficiency, classify events, and make accurate predictions, such as diagnosing diseases and prescribing medications. However, machine learning models are increasingly vulnerable to data poisoning attacks, which manipulate training data to degrade model accuracy and cause incorrect predictions. This study focuses on detecting data poisoning in e-health applications by simulating label-flipping attacks at different rates (5%, 25%, 50%, 75%) on breast cancer and diabetes datasets. The performance of machine learning models in disease detection was evaluated before and after poisoning, alongside their ability to detect poisoned data. Results show that models perform significantly better on clean data, with a marked deterioration at higher poisoning rates (50%–75%). The Random Forest (RF) and Gradient Boosting (GB) models proved most effective in detecting poisoned data, particularly at higher rates of poisoning. Conversely, the Logistic Regression (LR) and Multi-layer Perceptron (MLP) models tended to overgeneralize, leading to false positives, especially in the breast cancer dataset. This study highlights the importance of safeguarding ML models in e-health from data poisoning threats.