# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Anomaly Detection Performance  
Across the five synthetic patient profiles and multiple runs (24 h simulations each), the DIKWP-driven system consistently detected the injected health events with high accuracy. Table 3 summarizes the detection performance metrics aggregated over all test cases for two system variants. As shown in Table 3, both the proposed DIKWP system and the baseline achieve very high accuracy (97-98%), indicating that the vast majority of both event and no-event periods are correctly classified by both methods. However, the DIKWP agent attains a slightly higher precision than the baseline (95.0% vs. 90.5%), meaning it raises fewer false alarms (false positives) relative to the true events it detects. This advantage in precision can be attributed to DIKWP's ability to incorporate semantic context in its decision-making. For example, our system may suppress an alert if vital signs quickly return to normal or if it finds a plausible explanation that an observed anomaly is not critical. In contrast, the baseline's cloud-based approach triggers an alert whenever a threshold is crossed, without contextual reasoning, which leads to more frequent false alarms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Acc** & **P** & **R** & **F1** \\ \hline Baseline Cloud AI 1 & 98.0 & 90.5 & 85.0 & 87.7 \\ DIKWP Edge–Cloud AC 2 & 97.5 & 95.0 & 96.0 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Anomaly detection performance (%). Additionally, our system's recall is markedly higher than that of the baseline (96.0% vs. 85.0%), reflecting the ability of our approach to capture a greater proportion of true events. Recall represents the proportion of real events correctly identified, and the baseline's much lower recall suggests it missed a number of actual incidents--especially in complex scenarios where multiple anomalies occurred simultaneously. For instance, when a fall and tachycardia happened concurrently, the baseline's simplistic threshold-based logic sometimes became confused or prioritized one anomaly over the other, causing it to miss one of the events. In contrast, the DIKWP agent interpreted such concurrent anomalies as a combined pattern and successfully detected both, demonstrating the advantage of the multi-modal reasoning capability in our approach. Consequently, these improvements in precision and recall yield a substantially higher F1-score for DIKWP (95.5) compared to the baseline (87.7). The F1-score, being the harmonic mean of precision and recall, highlights the superior overall balance achieved by our system in capturing true events while minimizing false alerts. Figure 7 illustrates the recall performance comparison between the DIKWP Edge-Cloud AC system and the Baseline Cloud AI system across four event types: fall, tachycardia, hypotension, and fever. DIKWP consistently outperforms or matches the baseline system in all categories, demonstrating higher sensitivity and specificity due to its enhanced contextual understanding. * Fall: Out of 10 fall events in the test set, DIKWP system caught all 10 (100% recall) with 1 false alarm (it once thought a sensor glitch looked like a fall, which was a known limitation of our simple fall detection). Baseline caught 8/10 (missed 2 cases where fall had no immediate high heart rate, so baseline ignored them). * Tachycardia: We simulated 20 episodes of arrhythmia. DIKWP caught 19, missed 1 mild case; baseline caught 17, missed 3 (and raised 4 false alarms in cases of exercise-induced HR rise which were not arrhythmias, due to lack of context). * Hypotension: Simulated 5 cases, DIKWP caught 5, baseline 4. * Fever: Simulated 5 cases (fever defined as >38 \({}^{\circ}\)C prolonged). Both systems caught all eventually, but DIKWP raised earlier alerts because it noticed trend (gradual rise) and matched with slight HR increase, whereas baseline waited until a static threshold was breached. Early detection is a subtle benefit--not captured in just binary metrics, but qualitatively doctors would prefer earlier heads-up.