An optimal multi-disease prediction framework using hybrid machine learning techniques

The prediction of lifestyle diseases is a vital domain in healthcare informatics research. This task is primarily achieved using the widely available machine learning algorithms. However, the highdimensionality of data amplifies the computation complexity and significantly reduces the models’ efficiency. Conspicuously, we presented a multi-disease prediction strategy for intelligent decision support using ensemble learning. The proposed work leverages genetic algorithm-based recursive feature elimination and AdaBoost to predict two prominent lifestyle diseases. Alongside the proposed approach, various benchmark machine learning techniques are also trained and validated using selected features under k-fold cross-validation. The results reveal the effectiveness of the proposed methodology in predicting multiple diseases in comparison to past works.