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As a leading risk factor for chronic illnesses, obesity highlights the critical need for prompt diagnosis and active management. This research introduces an innovative expert system developed to precisely categorise obesity severity and offer individualised treatment advice. Utilising the SEMMA data mining framework alongside the tidymodels ecosystem, the study assessed five machine learning algorithms: decision tree, random forest, multinomial logistic regression (MLR), Naive Bayes, and support vector machine (SVM). Achieving the highest predictive accuracy (97.48%), MLR was chosen as the definitive model. The platform incorporates an intuitive R Shiny interface, promoting seamless real-time user engagement. Treatment protocols are formulated via if-then rule-based logic to guarantee customised advice for every obesity classification. A comparative evaluation emphasises the system's exceptional diagnostic precision and its functional utility in recommending treatments. By improving accessibility—especially for marginalised rural communities—this system bolsters public health by facilitating preliminary diagnosis, specific interventions, and forward-looking obesity care.

Intelligent systems increasingly influence decisions, allocate attention, generate recommendations, and mediate interactions across technical, organizational, and social environments. Yet trustworthiness is often approached through isolated interventions, such as improving model accuracy, adding post-hoc explanations, documenting datasets, or assigning nominal human oversight. This article proposes a trustworthy-by-design conceptual framework that treats trustworthiness as a system-level property requiring coordinated design and evaluation across the full intelligent-system lifecycle. The framework begins with system-purpose and risk-context definition, followed by stakeholder and responsibility mapping, data-integrity assessment, provenance and lineage review, data documentation and governance, model-reliability evaluation, robustness and uncertainty assessment, and bias and fairness review where relevant. It then connects explanation quality with human interpretability and decision usefulness, rather than assuming that technically generated explanations will necessarily support understanding. Human oversight, meaningful control, contestability, recourse, institutional accountability, traceability, auditability, monitoring, and feedback updating form additional interdependent layers. The framework’s principal contribution is to clarify how technical evidence must be connected to human decision conditions and institutional responsibility without treating any individual safeguard as sufficient. Data provenance does not establish data appropriateness, robustness does not prove safety, explanations do not guarantee interpretability, human presence does not ensure meaningful control, and governance arrangements do not independently establish accountability. The framework may support conceptual analysis, assurance-oriented evaluation, and lifecycle governance planning, but requires empirical validation, human-centered assessment, domain-specific interpretation, and continuing institutional review.

Multimodal intelligent systems combine evidence from text, vision, audio, sensors, and structured data, but their explanations often remain fragmented into separate saliency maps, feature scores, textual rationales, attention visualisations, or confidence indicators. Such outputs may describe isolated aspects of a model while obscuring how modality-specific evidence was represented, transformed, fused, and communicated. This article develops a conceptual multimodal explainability architecture organised around the principle of following evidence across modalities. The approach begins by distinguishing the evidential and representational characteristics of textual, visual, acoustic, temporal-sensor, and structured inputs. It then identifies attribution difficulties created by early, intermediate, late, and hybrid fusion, including modality dominance, missing evidence, conflicting inputs, representation alignment, and uncertainty about joint contributions. Evidence traceability is treated as an end-to-end requirement connecting input provenance, modality-specific representation, fusion operations, attribution processes, consistency assessment, uncertainty communication, and decision explanation. The architecture further incorporates human interpretation because technically detailed multimodal explanations may remain cognitively inaccessible, visually persuasive, incomplete, or poorly calibrated to user expertise and decision context. Its central contribution is a layered framework linking modality inventory, evidence classification, representation mapping, fusion attribution, provenance, consistency review, human-centred presentation, evaluation, and feedback updating. The architecture is intended to structure research and design reasoning rather than prescribe a particular model, explanation technique, evaluation metric, or deployment procedure. It does not establish that an explanation is faithful, that users understand it, that reliance is appropriate, or that the underlying intelligent system is trustworthy or ready for operational use.