# Artificial Intelligence Algorithms for Healthcare
###### Abstract
This research underscores the potential of transfer learning and CWT-based scalograms in improving HAR classification accuracy, particularly on small datasets, while also highlighting considerations for computational resources and potential limitations on larger datasets. The study \"Effective Heart Disease Prediction Using Machine Learning Techniques\" investigates heart disease prediction using machine learning techniques, employing a k-mode clustering algorithm and models such as Random Forest, Decision Tree, Multilayer Perceptron, and XGBoost on a dataset of 70,000 instances. The methodology involved preprocessing the data, including feature selection and reduction, and splitting based on gender to account for distinct disease manifestations. The Multilayer Perceptron model achieved the highest accuracy of 87.28% after hyperparameter tuning. This research underscores the potential of machine learning in developing targeted diagnostic strategies for heart disease, suggesting further exploration to enhance model generalizability and address limitations such as dataset specificity and variable comprehensiveness. The paper \"Towards a Flexible Assessment of Compliance with Clinical Protocols Using Fuzzy Aggregation Techniques\" introduces innovative metrics for assessing compliance with clinical protocols through fuzzy aggregation methods, particularly the Ordered Weighted Averaging operator and the Sugeno integral. These metrics consider the degree of compliance per activity, patient compliance, and the criticality of activities within healthcare settings. By applying these measures to case studies on glucose management and weaning protocols in Intensive Care Units, the research demonstrates their potential to provide more nuanced insights into protocol adherence. The findings suggest that these new methods can offer a comprehensive view of compliance, accommodating the variability and complexity of clinical practice and thus aiding in evaluating and improving healthcare processes. The authors of \"An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management\" delve into an innovative CNSS coordinate recognition algorithm to enhance epidemic management capabilities. This advanced approach significantly optimizes the identification process of targets within a predefined area, which is crucial for effective monitoring and control during health crises. Traditional methods, characterized by their dependence on extensive point-in-polygon computations, often lead to inefficiencies in tracking the movement of individuals or objects of interest. The proposed algorithm addresses these challenges by introducing a more streamlined and computationally efficient method. The research showcases the algorithm's robustness in spatial data management through detailed simulations, highlighting its applicability in various epidemic scenarios. The study's findings mark a significant contribution to computational geometry and offer practical implications for public health, suggesting a scalable and adaptable solution for epidemic surveillance and response efforts. This work paves the way for future advancements in technology-driven health crisis management, emphasizing the importance of innovative approaches in addressing the dynamic challenges of epidemic control. The paper \"Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence Models\" introduces a novel solution for rapidly identifying neurological complications in emergency rescue scenarios. Utilizing data from over 200,000 rescue cases, the study employs artificial intelligence models to analyze and extract meaningful information from raw rescue data. The paper discusses the development of multiple AI models, leveraging machine learning algorithms such as SVM, Random Forest, KNN, XGBoost, Logistic Regression, Naive Bayes, and Artificial Neural Networks to facilitate the quick identification of neurological complications.