Integrating Artificial Intelligence into Smart Healthcare Systems for Predictive Medicine and Public Health Governance

Abstract

With the evolution of Artificial Insultzation (AI) in smart healthcare system, this is changing the concept of predicting the health conditions, making clinical decisions and informing health government to people. The paper covers the perspectives of how AI will contribute to patient monitoring, disease prediction and personalised healthcare, population-level health management in the future. This work introduces various machine learning and deep learning models, such as Logistic Regression, random forest, Support Vector Machine (SVM), and Artificial neural networks (ANN) and tries its best to show how these models could be used to provide information regarding the classification of health risks for healthcare providers. A computational research framework is recommended to be activated in the context of healthcare data preprocessing, feature selection, model building and analyzing the performance of the model by using analysis tools based on python. This experimental analysis is the most successful model that can be accomplished using the Random Forest with prediction rate of 91.8 per cent and ROC-AUC value of 0.94 showing its potential for predicting the complex correlation between the clinical and demographic data. The paper will also discuss the application of AI in the area of disease surveillance, disease outbreak prediction, allocation of resources in the health care system, and forming of policy for the public health based on evidence. Significant issues found in implementation such as data privacy of patients, algorithmic bias, cybersecurity, compliance with regulations, and organisational preparedness are also noted in the research. Overall, this study proves the potential of responsible AI application to improve predictive healthcare, improve the performance of operations, and enable sustainable control of the health of the population.