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Recently, significant efforts have been dedicated to enhancing anomaly detection methods, especially concerning high-dimensional and distributed clinical datasets. The analysis of patient information in clinical environments highlights a strong emphasis on improving diagnostic precision, tailoring treatment strategies, and maximizing resource efficiency to boost health outcomes. However, this field encounters distinct difficulties, including sporadic data gathering, variable data quality, and structural differences unique to individual patients. To overcome these obstacles, this paper introduces an innovative hybrid framework that merges stochastic and heuristic techniques for detecting anomalies in clinical patient data. This approach leverages an HPO-optimized Density-Based Spatial Clustering of Applications with Noise to group patient exercise data, thereby streamlining the detection of abnormalities. Following this, an Interquartile Range-based stochastic technique filters out untrustworthy data points, guaranteeing that healthcare practitioners and diagnostic systems are supplied with only highly relevant and precise data. The main goal of this research is to provide medical experts and scholars with a reliable instrument for handling vast, high-dimensional clinical datasets, allowing for the successful separation and elimination of deviant data observations. Additionally, an advanced regression model was constructed utilizing Automated Machine Learning (AutoML) to evaluate the effectiveness of the ensemble technique for detecting abnormal patterns. Multiple statistical error evaluation methods are employed to verify the success of this hybrid strategy in conjunction with AutoML. Empirical findings indicate that applying this cutting-edge hybrid framework to patient rehabilitation data significantly boosts AutoML performance, yielding an average improvement of 0.041 in the score, thereby outperforming conventional regression models.

Explainable artificial intelligence increasingly supports decisions whose consequences extend beyond prediction, yet many explanations remain grounded in associations, feature contributions, saliency, or local perturbations. Such outputs may clarify model behaviour without establishing what would happen under intervention, why an outcome arose through a particular mechanism, or whether a proposed change is feasible and decision-valid. This article develops a conceptual causal explainability framework for moving from prediction-oriented explanation toward intervention-aware reasoning. The framework begins by separating predictive, associative, and model-behaviour claims from causal questions. It then connects explicit causal assumptions with causal structures, mechanistic reasoning, interventional explanation, counterfactual reasoning, confounding assessment, identifiability, sensitivity, external validity, uncertainty communication, and human interpretation. Particular attention is given to the distinction between model counterfactuals and real-world recourse, the risks created by unmeasured confounding, selection and collider bias, and the need to define decision boundaries before explanations inform action. Human users are treated as active interpreters whose causal literacy, expertise, cognitive constraints, and susceptibility to false causal confidence affect explanation usefulness. The main contribution is an integrated framework that organizes the evidential transition from prediction to intervention while preserving explicit no-guarantee boundaries concerning causal truth, intervention validity, recourse, identifiability, human understanding, decision validity, and deployment readiness. The framework is intended to support disciplined conceptual development and evaluation planning, not to prescribe a universal causal model, intervention procedure, or operational decision system.

Intelligent systems increasingly provide predictions, recommendations, and explanations in decisions where uncertainty can materially affect interpretation and action. Yet many explainable systems foreground predicted outcomes or feature contributions while inadequately communicating what the model does not know. This article proposes a conceptual uncertainty-aware explainable decision-intelligence framework organized around four connected responsibilities: calibrating confidence, disclosing ambiguity, communicating decision-relevant risk, and supporting selective prediction or abstention when available evidence is insufficient. The framework treats uncertainty as a trustworthiness requirement rather than a secondary technical output. It distinguishes aleatoric uncertainty arising from irreducible variability, epistemic uncertainty associated with limited knowledge or evidence, and distributional uncertainty arising when operating conditions depart from those represented during development. It further connects confidence calibration and miscalibration review with prediction qualification, rejection, abstention, deferral, and human review. Ambiguity disclosure and risk communication are positioned as interpretive functions that should clarify scope limitations, competing interpretations, possible consequences, and appropriate next actions without implying that communication alone guarantees understanding. Human decision support is addressed through escalation pathways, oversight, trust calibration, and evaluation of overreliance, underreliance, automation bias, and cognitive burden. The principal contribution is an integrated framework that links uncertainty identification, calibrated interpretation, selective system behavior, human review, monitoring, and feedback updating. The framework supports conceptual development and domain-specific evaluation but does not independently establish correctness, safety, decision validity, ethical sufficiency, legal compliance, or deployment readiness.