Fusion-ADiNet: a multi-level framework for enhanced diabetes and Alzheimer’s disease detection using chimp-whale fusion estimation

Rising cases of diabetes and AD are the two biggest health-related issues world-wide; this greatly hampers the quality of life among the afflicted persons. Early identification of diabetes most especially correlated with neurodegenerative conditions such as Alzheimer's guarantees early intervention and hence proper management. However, the existing approaches for disease detection have been suffering from a series of limitations in poor diagnostic accuracy, high computational complexity, and the lack of an effective model that could properly handle the intricate correlations between diabetes and AD. Most of the existing methods for diabetes and AD detection rely on traditional machine-learning algorithms or heuristic optimization approaches, which are not capable of handling high dimensionality and complex clinical data. The models also find it extremely difficult to represent the subtle relationship between the two diseases, leading to unsatisfying performance in practical applications. It is therefore imminent to develop much more advanced methodologies with integration that could improve accuracy in prediction, enabling better decision-making in clinics. To overcome the limitations of existing methods, in this paper, we propose a new approach called Fusion-Alzheimer's Diabetes Network (Fusion-ADiNet) by introducing a multi-level fusion framework for disease detection. The main novelty in this method lies in the newly designed Chimp-Whale Fusion Estimator (CWFE) optimization algorithm. Furthermore, the Fusion-ADiNet framework is quite flexible, so extending it to other diseases or datasets in the future will be very easy. This work contributes much to the field of healthcare analytics and opens new perspectives toward more effective diagnostic tools for timely detection of diabetes and Alzheimer's disease.