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The rising occurrence of long-term illnesses requires inventive and effective healthcare solutions, and the incorporation of Internet of Things (IoT) technologies holds significant potential in revolutionizing conventional medical monitoring. This study presents an innovative method called Adaptive Federated Learning for Chronic Disease Prediction (AFL-CDP), which is specifically designed for real-time medical Internet of Things (IoT) applications. The main objective is to enhance both privacy and accuracy in the surveillance of chronic diseases. AFLCDP utilizes federated learning, a decentralized approach to machine learning that allows for model training on multiple edge devices without the need to transfer raw data to a central server. This not only mitigates privacy concerns related to sensitive medical data but also improves the precision of predictive models by assimilating information from various data sources. The AFL-CDP adaptability enables the ongoing improvement of the predictive model using changing patient data, resulting in personalized and timely forecasts for chronic diseases. In order to improve privacy in IoT devices with limited resources, the study integrates the utilization of SPECK, an advanced technique for preserving privacy. SPECK utilizes secure aggregation and encryption mechanisms to safeguard patient data throughout the federated learning process, guaranteeing confidentiality while preserving the integrity of the model. Ensuring data security and patient privacy are of utmost importance, particularly in the field of medical IoT. The proposed methodology is assessed using a dataset that consists of real-time medical Internet of Things (IoT) data for the purpose of monitoring chronic diseases. The model's performance is evaluated using the Area Under the Curve (AUC) accuracy metric, and AFLCDP achieves an impressive AUC accuracy of 94.37%. This showcases the efficacy of the federated learning framework in capturing the fundamental patterns in varied and decentralized healthcare data. To summarize, this study presents an innovative and strong approach for realtime medical Internet of Things (IoT) applications, highlighting the significance of privacy and precision in monitoring chronic diseases. The combination of AFL-CDP and SPECK offers a thorough method that not only satisfies the strict privacy demands of healthcare data but also achieves a high level of predictive precision, establishing the basis for enhanced patient results and personalized healthcare interventions. [ABSTRACT FROM AUTHOR]

Hypertension affects millions of patients worldwide. Persistent elevation affecting the arteries and veins in eye can be the most accessible to clinically detect undiagnosed hypertension. Aim: To know the incidence of retinopathy due to hypertension in both sex and various age groups. The severity of hypertension and its relation with fundus changes is observed. Material and Methods: The Study was carried out at the department of ophthalmology in Shri Guru Gobind Singh Hospital. Detailed ophthalmic evaluation including dilated fundus and systemic examination of the patient is carried out in detail. Thereafter attempt is made to correlate the Fundus findings with age, sex, past history etc. For hypertensive retinopathy Keith-Wagener-Barker Classification is used. Result: Hypertension is most common in 5th to 6th decade of life. Males are more affected than females. Male to female ratio is 1.72. Fundus changes are observed maximum in grade II (26, 44.8%) followed by Grade I (15,21.9%), Grade III(11,19%) and minimal in Grade IV(6,10.3%).With incidence of hypertensive retinopathy in different age group shows that Grade II more number of cases 26 (44.8%) in 41-50 years of age group showing 8 cases. In complication of hypertensive retinopathy, 10 cases of macular edema, 6 cases of disc edema, 1 case of central retinal vein occlusion and 1 case of branch retinal vein occlusion are found. Conclusion: Incidence of hypertensive retinopathy is increased as age is increased with maximum presentation in 5th to 6th decade of life with male dominance with male:female ratio 1.72. Hypertensive retinopathy grade II is the most common retinopathy found. Higher systolic and diastolic blood pressure was associated with more severe retinopathy. Grade-III and Grade-IV hypertensive retinopathy was maximally associated with systemic involvement. [ABSTRACT FROM AUTHOR]