## 4 Discussion
### Binary Classification Using Logistic Regression  
The MPT\(-\) model's predictions tended to be biased toward admission, with 54 admission predictions and 37 discharge predictions. The input data set was slightly biased towards admission recordings, with 47 recordings from admission and 44 from discharge. To adjust this bias in future work, it may be fruitful to adjust the probability threshold needed to predict discharge. Currently, any data point with discharge probability over 0.5 received a discharge prediction, but that 0.5 threshold could be optimized with more data. The MPT\(+\) model's predictions were more balanced than those of the MPT\(-\) model, with 22 admission and 20 discharge predictions. The MPT\(+\) model's predictions were also more accurate overall. This comparison suggests that MPT is a useful feature to identify decompensated heart failure and is worth investigating more closely in future work. The feature weights and odds ratios provide information about each feature's predictive power. Feature weights farther from 0 indicate that the model's prediction is based relatively more on that feature. It is noted that results may be sensitive to the study's regularization method applied to the model coefficients. The odds ratios for the MPT\(-\) model indicate that a discharge prediction was associated with increased mean F0 in vowels; greater phonatory stability (decreased F0 SD, increased CPP, and decreased CPP SD) in vowels; increased crack percent in sentences; faster speech rate in the Rainbow Passage; and reduced, more regular pauses in the Rainbow Passage and spontaneous speech. More specifically, in the Rainbow Passage, the percent of time devoted to speaking rather than pausing increased and the standard deviation of phrase durations decreased. The mean and median phrase duration both increased for spontaneous speech. These results broadly support our hypotheses that voice and speech characteristics at discharge would have reduced instability, increased F0, longer phrase durations, and less pausing. In this study, as in [28], we found that post-treatment voices contained a higher proportion of creaky voice quality than did pre-treatment voices. This finding may be counterintuitive at first: creaky phonation is a form of vocal instability, and post-treatment voices tended to be more stable. However, creaky voice production requires the vocal fold superior layer to be slack, and vocal fold edema due to the presence of ADHF could stiffen  
Figure 4: Distributions of microphone-based maximum phonation time for speakers’ first day before treatment (First), last day after treatment at discharge (Last), and first-to-last day changes (Change). Each box–whisker plot indicates median (horizontal line within box), interquartile range between 25 th and 75 th percentiles (box bounds), minimum value within 1.5 times the interquartile range away from the bottom of the box (lower whisker bound), maximum value within 1.5 times the interquartile range away from the top of the box (upper whisker bound), and outliers that exist outside these minimum and maximum values (+ symbols). the vocal folds and make that slackening more difficult to achieve. After treatment, when the edema is reduced, it is possible that creaky voice production may have returned to its higher, baseline characteristics. The MPT+ model's optimized \(\lambda\) was higher, so fewer features had non-zero weights after regularization. In this model, the probability of a recording being classified as at discharge was associated with increased F0 SD in sentences, increased MPT, and longer phrase durations in spontaneous speech. Notably, the MPT\(-\) model showed a _decrease_ in F0 SD for sustained vowels, while the MPT+ model showed an increase in F0 SD in CAPE-V sentences. These divergent changes might appear contradictory but are likely driven by different mechanisms. In sustained vowel phonation, the speaker generally maintains a stable F0 target, although even healthy speakers naturally produce some variation around the target.