The field of predictive maintenance and diagnostics is rapidly evolving, with a focus on developing innovative techniques to improve the accuracy and efficiency of fault detection and prevention. Recent studies have explored the application of machine learning algorithms, such as supervised learning models, to diagnose the condition of electric motors and predict soil macronutrient levels. Additionally, research has been conducted on the use of telemedicine and predictive algorithms for the care and prevention of patients with chronic heart failure.

Noteworthy papers include: Predictive Maintenance of Electric Motors Using Supervised Learning Models, which compared the performance of various machine learning algorithms for predicting motor health. PrediHealth: Telemedicine and Predictive Algorithms for the Care and Prevention of Patients with Chronic Heart Failure, which presented a unified digital healthcare platform for managing chronic heart failure. SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis, which proposed a fast post-hoc attribution method for interpreting intelligent fault diagnosis. Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI, which evaluated the performance of fine-tuned CatBoost and nature-inspired algorithms for detecting chronic kidney disease. Explainable AI for building energy retrofitting under data scarcity, which presented an AI-based framework for recommending energy efficiency measures for residential buildings. Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals, which systematically evaluated and optimized machine learning models for predicting metabolic syndrome.