Applied Machine Learning Across Vision, Engineering Optimization, and Healthcare Decision Support: A Cross-Domain Technical Synthesis of Models, Evaluation, and Deployment

Abstract

Machine learning (ML) techniques increasingly transfer across domains that historically evolved separately: computer vision for perception, regression models for engineering optimization, and rule-based or data-driven systems for clinical decision support. Despite distinct end goals, these domains share technical constraints-limited labeled data, distribution shift, safety requirements, and strong demands for reliable evaluation and deployment. This paper synthesizes applied ML practices across three representative settings: (i) depth-related perception as a supervised or self-supervised regression problem, (ii) surrogate modeling for optimization in engineering design, and (iii) decision support systems in healthcare that must balance interpretability, robustness, and privacy. We connect discriminative deep networks, generative adversarial networks (GANs) for data augmentation, support vector regression (SVR) and surrogate modeling, and expert systems within a unified pipeline perspective. We provide common mathematical formulations, a practical evaluation checklist, and cross-domain reporting guidance. Figures and tables summarize design trade-o!s, metrics, and recommended ablations.

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