## 3 Results
### Prediction Analysis for Improvement in IAQ by Machine Learning  
The SAR logged a total of 4689 alarms for ultrafine dust. In 3775 cases (80.5%), the IAQ either 'improved' or'remained good' 30 min after the alarm. Following level 2 alerts, good air quality was maintained after 30 min in 91.3% of cases. For level 3 alerts, indicating 'poor' air conditions, 35.1% of cases returned to 'good' air conditions after 30 min. After level 4 alerts, 23.9% of cases returned to 'good' air conditions after 30 min. (Table 4). Figure 5: Daily trend of air quality alert occurrences per hour (averaged, 11 patients). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Week 1** & **Week 2** & **Week 3** & **Week 4** \\ \hline Good (Stage 1 or 2) & 1.121 & 1.262 & 1.260 & 1.121 \\ Bad (Stage 3 or 4) & 0.580 & 0.509 & 0.464 & 0.298 \\ \hline \end{tabular}
\end{table} 
Table 3: The average hourly frequency of SAR air quality alerts (by week). Table 5 summarizes the accuracy, sensitivity, specificity, and area under the curve (AUC) of each prediction model. By comparing AUCs, all prediction models have similar explanatory power, ranging from 0.82 to 0.85. However, the random forest model (0.85) best predicted the IAQ improvement in terms of the ultrafine dust variable. The random forest model has the characteristic of generating a bootstrap sample using training data, selecting only some input variables to create a decision tree, and identifying a linear combination of variables to make a final learner [55]. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Not**} & \multicolumn{2}{c}{**Improved/Remain**} \\ \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Sum**} & \multicolumn{2}{c}{**Improved/Worsened**} & \multicolumn{2}{c}{**Good**} \\  & & **(Stage 3 or 4)** & \multicolumn{2}{c}{**(Stage 1 or 2)**} \\ \cline{3-5}  & & **N** & **(\%)** & **N** & **(\%)** \\ \hline Intervention Stage 2 Alert & 3800 & 329 & (8.7) & 3471 & (91.3) \\ Intervention Stage 3 Alert & 818 & 531 & (64.9) & 287 & (35.1) \\ Intervention Stage 4 Alert & 71 & 54 & (76.1) & 17 & (23.9) \\ Week 1 to 2 & 1300 & 238 & (18.3) & 1062 & (81.7) \\ Week 3 to 4 & 1100 & 192 & (17.5) & 908 & (82.2) \\ Week 5 to 6 & 1107 & 225 & (20.3) & 882 & (79.7) \\ Week 7 to 8 & 1182 & 259 & (21.9) & 923 & (78.1) \\ Alert time AM 0 to 6 ’ o clock & 508 & 106 & (20.9) & 402 & (79.1) \\ Alert time AM 7 to 12 ’ o clock & 1823 & 386 & (21.2) & 1437 & (78.8) \\ Alert time PM 12 to 6 ’ o clock & 1379 & 208 & (15.1) & 1171 & (84.9) \\ Alert time PM 7 to 12 ’ o clock & 979 & 214 & (21.9) & 765 & (78.1) \\ Aged over 75 & 2346 & 531 & (22.6) & 1815 & (77.4) \\ Living Alone & 2309 & 499 & (21.6) & 1810 & (78.4) \\ CAT high (baseline) & 1431 & 299 & (20.9) & 1132 & (79.1) \\ CAT mid (baseline) & 1142 & 210 & (18.4) & 932 & (81.6) \\ CAT low (baseline) & 2116 & 405 & (19.1) & 1711 & (80.9) \\ CAT high (end point) & 1431 & 299 & (20.9) & 1132 & (79.1) \\ CAT mid (end point) & 1791 & 279 & (15.6) & 1512 & (84.4) \\ CAT low (end point) & 1467 & 336 & (22.9) & 1131 & (77.1) \\ MRC 0 to 1 (baseline) & 2980 & 582 & (19.5) & 2398 & (80.5) \\ MRC 2 to 3 (baseline) & 2833 & 566 & (20.0) & 2267 & (80.0) \\ BSCC 0 to 2 (baseline) & 3465 & 660 & (19.0) & 2805 & (81.0) \\ BSCC 0 to 2 (end point) & 1924 & 392 & (20.4) & 1532 & (79.6) \\ TAI good (end point) & 3326 & 664 & (20.0) & 2662 & (80.0) \\ TAI intermediate (end point) & 1170 & 213 & (18.2) & 957 & (81.8) \\ TAI poor (end point) & 193 & 37 & (19.2) & 156 & (80.8) \\ PHQ-9 none (baseline) & 4086 & 780 & (19.1) & 3306 & (80.9) \\ PHQ-9 none (end point) & 2733 & 434 & (15.9) & 2299 & (84.1) \\ EQ-5D perfect (baseline) & 1914 & 382 & (20.0) & 1532 & (80.0) \\ EQ-5D perfect (end point) & 1461 & 288 & (19.7) & 1173 & (80.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of features for machine learning and logs of IAQ.