A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides a comprehensive overview of FL, focusing on its integration with the IoT. We delve into the motivations behind adopting FL for IoT, the underlying techniques that facilitate this integration, the unique challenges posed by IoT environments, and the diverse range of applications where FL is making an impact. Finally, this submission also outlines future research directions and open issues, aiming to provide a detailed roadmap for advancing FL in IoT settings.

The lungs are the center of breath control and ensure that every cell in the body receives oxygen. At the same time, they filter the air to prevent the entry of useless substances and germs into the body. The human body has specially designed defence mechanisms that protect the lungs. However, they are not enough to completely eliminate the risk of various diseases that affect the lungs. Infections, inflammation or even more serious complications, such as the growth of a cancerous tumor, can affect the lungs. In this work, we used machine learning (ML) methods to build efficient models for identifying high-risk individuals for incurring lung cancer and, thus, making earlier interventions to avoid long-term complications. The suggestion of this article is the Rotation Forest that achieves high performance and is evaluated by well-known metrics, such as precision, recall, F-Measure, accuracy and area under the curve (AUC). More specifically, the evaluation of the experiments showed that the proposed model prevailed with an AUC of 99.3%, F-Measure, precision, recall and accuracy of 97.1%.

A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.

Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney function over time. It describes a clinical entity that causes kidney damage and affects the general health of the human body. Improper diagnosis and treatment of the disease can eventually lead to end-stage renal disease and ultimately lead to the patient’s death. Machine Learning (ML) techniques have acquired an important role in disease prediction and are a useful tool in the field of medical science. In the present research work, we aim to build efficient tools for predicting CKD occurrence, following an approach which exploits ML techniques. More specifically, first, we apply class balancing in order to tackle the non-uniform distribution of the instances in the two classes, then features ranking and analysis are performed, and finally, several ML models are trained and evaluated based on various performance metrics. The derived results highlighted the Rotation Forest (RotF), which prevailed in relation to compared models with an Area Under the Curve (AUC) of 100%, Precision, Recall, F-Measure and Accuracy equal to 99.2%.