A Collaborative Framework for Disease Prediction Using Machine Learning

Abstract

Machine learning has become an important tool in modern healthcare, especially for predicting diseases early and improving patient outcomes. However, most current machine learning models are developed using isolated data from individual hospitals, which limits their ability to generalize to wider populations. These models also face challenges such as data bias, limited accuracy, and strict data privacy laws that prevent institutions from sharing patient information. To address these issues, this paper proposes a Collaborative Framework for Disease Prediction Using Machine Learning (CFMLDP). The framework uses federated learning, differential privacy, and secure multiparty computation to allow multiple healthcare institutions to train shared models while keeping patient data private. The framework includes stages such as data preprocessing, privacy-preserving local training, secure model aggregation, bias detection, and fairness auditing. It was tested using various disease categories and achieved high performance with both overall accuracy and average F1-score recorded at 93%. These results show that collaborative machine learning can produce reliable, accurate, and privacy-conscious disease prediction models.

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