This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems.

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers.

The efficiency optimization methods for natural coagulants are often restricted due to non-scientific trial-and-error approaches. They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. To overcome these obstacles, this research paper proposes advanced hybrid models in machine learning to enhance flocculation efficiency. We use the CatBoost model with the NTK to learn the intricate nonlinear interactions among jet velocity, mixing time, coagulant dose, pH, and turbidity. CatBoost is effective for dealing with categorical data like diverse coagulants. Meanwhile, NTK boosts the model’s generalization capability, especially when the sample size becomes small or experimental datasets are applied. Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. Reinforcement learning techniques—that include DDPG and SAC for dynamic optimization of jet velocity, mixing time, and coagulant dosage—optimize the model in real time. Utilizing NAS and Hyperband to automate model tuning, the timestamp was reduced by 40%. The proposed models heavily improve the efficiency of the flocculation process by 20–25% and allow for a good predictive accuracy of 95–97%. Paramount, however, is that the model has interpretability properties assured by SHAP and counterfactual explanations, which would give actionable insights into the most influencing factors on the efficiency of flocculation. This work represents a substantial advancement for the discipline since it introduces robust, interpretable, and real-time optimization methods to offer a practical tool through which improvement of water treatment processes would be made both sustainable and efficient.

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin–Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.