AI-Driven Predictive Analytics for Intelligent Healthcare Monitoring and Early Disease Detection

Abstract

Incorporation of AI and ML into healthcare monitoring systems has driven the enhancement of the most significant progress in patient care, such as early detection, personalized care, and cost efficiency. This paper presents a new AI solution of a predictive analytics framework specifically designed for healthcare systems with an emphasis on the respiratory imbalance severity. Our experimental setup includes a combination of different machine learning algorithms, including Support Vector Machines (SVM), AdaBoost, and state-of-the-art advanced ensembles like Stacked Ensembles, as well as deep learning algorithms, including TabNet. Applying strict feature selection methods, data preprocessing with feature scaling, SMOTE, and other balancing methods guarantees the efficiency of the developed framework in terms of predictive performance and model interpretability. Concerning the prioritization of features, key findings show the importance of the respiratory rate and the type of first diagnosis when developing clinical models. The Stacked Ensemble classifiers, based on SVM, KNN and AdaBoost, provided nearly perfect accuracy of 99.92%, which was far superior to individual classifiers. Exploratory data analysis or EDA, and reducing dimensions with the help of PCA, gave additional specificity in data representation for building performance and scalable, efficient predictive models. Performance metrics integral to the confusion matrices and AUC scores provide additional validation that the framework is valuable in minimizing sources of misclassification error and providing actionable insights for operational decision-making. A use case of this research is the innovative capabilities of AI-enabled systems to revolutionize healthcare observation by identifying severe states at an early stage for certain conditions using revolutionary clinical procedures. It also shows the limitations of the study, including ethics issues, data normalization, and interdisciplinarity, to advance recommendations for future research to incorporate more sophisticated approaches to deep learning. Thus, the work demonstrated in this study brings together technological advancement and clinical ideas to make healthcare more intelligent in the future.