• Mohd Adli MD Ali International Islamic University Of Malaysia
  • Edre Mohammad Aidid International Islamic University Malaysia image/svg+xml
  • HAFIDZUL ABDULLAH International Islamic University Malaysia image/svg+xml


Artificial Neural Network, Federated Learning, Ensemble Learning, Chest Radiograph


Multiple research has shown that deep artificial neural networks (ANN) can assist physicians in diagnosing a patient with greater accuracy and sensitivity. ANN applications are not limited to just the classification of diseases but also include image segmentation, tumor localization, and mortality rate predictions. Nonetheless, the great march of success by ANN is only possible by the availability of an open medical dataset. Only with such open datasets can developers build, train and test ANN models to obtain higher accuracy. However, there is yet an open medical dataset in Malaysia that can be used to validate ANN performance on local patients. This may be due to local medical institution's hesitance to release any medical images and records to respect patient's confidentiality. One way around this is to adopt the Ensemble or Federated Learning system. In federated learning, medical institutes share their locally trained ANN model's weight and bias to create a single federated level ANN model. While in ensemble learning, multiple ANN models are used to vote on the most probable class for each data. In both systems, no sharing of patient's data is required. In our experiment, we tested the capability of 25 ANN models to classify chest radiograph images into three classes; normal, bacterial pneumonia, and viral pneumonia. Each ANN model is given a training dataset that is random in size and class ratio. The result obtained from the experiment shows that the federated system obtains the highest score in all measured metrics. It obtains a score of 0.76, 0.72, and 0.72 for average weighted precision, weight sensitivity, and F1, respectively. It also has the lowest standard deviation in all performance metrics compare to other learning systems. The result obtains here further strengthens the notion that if Malaysia wants to adopt a national-level artificial intelligent system for medical purposes, it should utilize the federated learning system at its core. It ensures Malaysia has an artificial intelligence system that respects patient's privacy while maintains its robustness.


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How to Cite

MD Ali, M. A., Mohammad Aidid, E., & ABDULLAH, H. . (2021). RESPECTING PATIENT PRIVACY WITH FEDERATED ARTIFICIAL INTELLIGENCE . Journal of Information Systems and Digital Technologies, 3(2), 84–93. Retrieved from