Metaheuristic Optimization Review

The present research investigates the role of machine learning models in forecasting the course of Lyme disease and improving diagnostics by looking for environmental, host and anthropogenic factors contributing to the rise and fall of the tick population and disease outbreaks. With the popularization of ecological models and artificial intelligence-based techniques such as neural networks and random forests, it has become possible to efficiently and accurately over various risk maps that relate to ticks' location and distribution, which is an essential aspect of improving public health management issues. These models integrate climate and demographic data as well as host-pathogen interaction data and help understand the distribution of high-risk areas and the dynamics of the diseases, thus facilitating the management of tick-borne illness. This approach also illustrates the significance of predictive diagnostics for early disease detection, allowing for interventions and preventive measures only on relevant population sub-groups. Ultimately, this study considers the possibilities machine learning offers in managing Lyme disease, articulating the implications of these conclusions for the preparedness for health emergencies on a more global scale.

This systematic review explores the use of artificial intelligence (AI) and machine learning (ML) during the COVID-19 disease outbreak. AI/ML models may interpret medical images, auditory input, and patient records to diagnose early enough, thus enhancing the likelihood of positive patient outcomes. Coupled with optimization algorithms, deep learning methods have predicted COVID-19 from chest X-rays and CT scans with unprecedented high accuracy. This review, therefore, synthesizes the existing literature and looks at the significant emphases, gaps, and potential trends of applying AI in diagnosing COVID-19 and forecasting outbreaks. Further, the advancement of AI and ML in this domain needs to be known to enhance global preventive diagnostic techniques for future pandemics.

This review aims to discuss the use of AI and ML in diagnosing and managing neurodegenerative diseases, with particular emphasis on AD and MCI. Emerging innovations present in depth the effectiveness of using ML models such as SVM, random forests, CNNs, and new frameworks such as quantum-classical neural networks on data obtained from MRI imaging, EEG signals, genetic makers and sociodemographic data. Widely used research findings demonstrate that these tools offer seemingly higher detection rates, sensitivity, and specificity than traditional diagnostic techniques for identifying and diagnosing early-stage illnesses. Some of them are techniques based on analyzing EEG time-frequency bands, combining MRI and PET data integration approaches, and creating telemedicine services to overcome geographical barriers. Furthermore, interpretable AI models improve clinical relevance by providing decision and trust support among practitioners. While these achievements are notable, the following limitations need to be noted, thus making it easier to establish the generalizability of the results and ways of using datasets that are free from bias and difficulties associated with applying AI in clinical settings. There are pressing questions regarding patients' rights and privacy, the issue of homogenization and standardization of data, and the distribution and accessibility of AI tools across industries as well as within the same region. More studies should be conducted to expand AI applications, use a more diverse dataset, and promote cooperation between representatives of various fields of science to ensure that technological advancement meets clinical demands. It also includes new methods like Vision Transformers and Quantum Computing Enhanced Deep Learning to overcome diagnostic issues in time-consuming and multi-parametric data analysis. These gaps can be closed with the help of AI and ML to enhance diagnostic accuracy, select the right treatment strategy, and risk assessment for the long-term management of NDs. In conclusion, this review similarly reaffirms how stunning AI's role is in improving future neurodegenerative disease care. For this reason, the deployment process must be done sensibly to enhance the patient's value most appropriately.

This paper explores the potential of machine learning (ML) in revolutionizing the screening and prognosis of dengue fever, a pervasive viral illness transmitted through the bite of infected mosquitoes prevalent in tropical and subtropical areas. Historically, traditional approaches to monitoring outbreaks have been hampered by a lack of precision and timing, creating an opportunity for machine learning to rectify datasets and uncover patterns that enhance accuracy. The paper introduces Random Forests, Support Vector Machines, Neural Networks, and combined classification models, along with their advantages, disadvantages, and the potential for incorporating external data such as climatic factors, population data, real-time Twitter data, etc. The results demonstrate significant increases in accuracy from the models, but it is clear that their applicability is contingent on localized approaches suitable for the regions. This underscores the importance of the quality and completeness of data used in the models. Current research indicates that data availability and the refinement of these models require a collective approach. The work underscores the potential of ML to redefine the paradigm of outbreak management in dengue and other vector-borne diseases, offering hope for improved public health worldwide.

The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.