Behavioral analysis across multiple domains using machine learning and deep learning models

Abstract

<span>Behavioral analysis.<span lang="EN-US">using machine learning (ML) and deep learning (DL) has become critical across healthcare, finance, cybersecurity, education, and marketing. This systematic review synthesizes advancements in ML- and DL-driven behavioral analysis (2019-2025) across five key domains. Our findings reveal that Deep Learning techniques achieve superior predictive accuracy (85-97% in healthcare imaging anomaly detection), while Machine Learning remains preferred for interpretability in finance (accuracy: 78-92%, with explainability advantage). A major trade-off emerges: DL models demonstrate higher accuracy but require substantial labeled data and computational resources, whereas ML models offer transparency but limited scalability. This review contributes by: (1) systematically analyzing domain-specific performance metrics and model evolution; (2) providing comparative synthesis of ML vs. DL approaches with quantitative benchmarking; (3) identifying critical challenges (data quality, privacy, algorithmic bias, interpretability); and (4) proposing actionable future directions, including Explainable AI, Federated Learning, and multimodal fusion. We adopt PRISMA-guided methodology examining 100+ peer-reviewed studies, revealing that hybrid ML-DL architectures represent the emerging best practice for balancing accuracy with interpretability.</span></span>

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