Deep Learning Model for Diagnosis of Chronic Diseases

Abstract

The increasing prevalence of chronic diseases and aging populations has created significant challenges for healthcare systems worldwide.To meet these challenges, there has been a growing interest in leveraging advanced technologies, such as data fusion and cloud storage, to enable more efficient and effective healthcare services.The design methodology employed for this innovative system is object-oriented analysis and design, providing a structured and systematic framework for the development process.Furthermore, the utilization of the Python programming language enhances the system's efficiency, scalability, and maintainability.By integrating a data fusion model, the system combines data from multiple sources to provide a more accurate and holistic view of patient health, thus enhancing the diagnostic process.This fusion of diverse data types, coupled with the robust CNN architecture, ensures a high level of precision and reliability in disease detection.This dissertation highlights an approach to conducting checks on various chronic diseases, including malaria, typhoid, heart disease, diabetic retinopathy, liver disease, and fetal health, utilizing Convolutional Neural Networks (CNN).The developed model exhibits an exceptional accuracy rate of 99.98%, underscoring its effectiveness in disease detection.The findings of this research represent a significant leap forward in leveraging advanced technologies for precise and comprehensive chronic disease diagnostics, with implications for improving healthcare outcomes and patient well-being.