Performance Comparison of ML Algorithms for Sustainable Smart Health Systems

Abstract

Disease prognosis holds immense significance in healthcare due to its potential to greatly improve patient outcomes through early and precise diagnosis. Machine learning (ML) algorithms provide a robust avenue for disease prediction, employing patient data analysis to detect intricate patterns of specific ailments. Machine learning algorithms adeptly handle intricate and extensive datasets, uncovering latent patterns often eluding human observation. By considering diverse symptoms and their permutations, ML models yield precise forecasts concerning the probability of distinct diseases. The investigation begins by laying a basis in sustainable development concepts, recognising the need of resource optimisation, energy efficiency, and minimal environmental effect in the context of healthcare technology. Categorically, disease prediction methodologies fall under supervised and unsupervised learning categories, involving training algorithms on annotated datasets containing symptoms and corresponding diagnoses. These trained models can then anticipate diseases based on novel symptom profiles.