Background: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features.

Objective: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC).

Method: Acoustic features derived from Swedish utterances of the vowel [a:], recorded via mobile phones from 48 age-matched participants (24 COPD, 24 HC; equal gender distribution), were analyzed. To reduce bias from varying recording counts, features were aggregated by averaging 10 randomly selected recordings per participant over 100 iterations. Vowel articulation was visualized in the vowel quadrilateral space using F1 (tongue height) and F2 (tongue advancement). Group differences were assessed using the Shapiro-Wilk test, Mann-Whitney U test (α = 0.05), Benjamini-Hochberg (BH) and Bonferroni corrections, Permutational Multivariate Analysis of Variance (PERMANOVA) test, and Cliff's Delta (δ).

Results: Of 101 features, 29 remained significant after BH correction and one after Bonferroni. Multivariate testing (p = 0.019) showed group separation. Additionally, 34 features demonstrated large effect sizes, suggesting potential as digital biomarkers.

Conclusion: Voice data recorded via mobile phones capture meaningful acoustic differences associated with COPD. These findings support the integration of voice-based assessments into eHealth platforms for noninvasive COPD screening and monitoring, which is pending further validation on larger populations.

Clinical Trial: NCT06705647

Background: As the older population grows, so does the prevalence of cognitive impairment, emphasizing the importance of early diagnosis. The Mini-Mental State Examination (MMSE) is vital in identifying cognitive impairment. It is known that degraded oral health correlates with MMSE scores <= 26.

Objective: This study aims to explore the potential of using machine learning (ML) technologies using oral health and demographic examination data to predict the probability of having MMSE scores of 30 or <= 26 in Swedish individuals older than 60 years.

Methods: The study had a cross-sectional design. Baseline data from 2 longitudinal oral health and ongoing general health studies involving individuals older than 60 years were entered into ML models, including random forest, support vector machine, and CatBoost (CB) to classify MMSE scores as either 30 or <= 26, distinguishing between MMSE of 30 and MMSE <= 26 groups. Nested cross-validation (nCV) was used to mitigate overfitting. The best performance-giving model was further investigated for feature importance using Shapley additive explanation summary plots to easily visualize the contribution of each feature to the prediction output. The sample consisted of 693 individuals (350 females and 343 males).

Results: All CB, random forest, and support vector machine models achieved high classification accuracies. However, CB exhibited superior performance with an average accuracy of 80.6% on the model using 3 x 3 nCV and surpassed the performance of other models. The Shapley additive explanation summary plot illustrates the impact of factors on the model's predictions, such as age, Plaque Index, probing pocket depth, a feeling of dry mouth, level of education, and use of dental hygiene tools for approximal cleaning.

Conclusions: The oral health parameters and demographic data used as inputs for ML classifiers contain sufficient information to differentiate between MMSE scores <= 26 and 30. This study suggests oral health parameters and ML techniques could offer a potential tool for screening MMSE scores for individuals aged 60 years and older. 

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide, with high underdiagnosis rates due to limitations in current diagnostic methods such as spirometry. This doctoral thesis explores the potential of voice as a digital biomarker to support the assessment of COPD, guided by the principles of Applied Health Technology (AHT), which emphasizes interdisciplinary collaboration and real-world applicability.

The research includes four interconnected studies. Study I presents a systematic literature review of machine learning (ML) applications for voice-affecting disorders, identifying COPD as underrepresented in current research. Study II addresses this gap by collecting a new dataset of vowel [a:] recordings from Swedish-speaking COPD patients and healthy controls once a week in self-determined quiet settings. Voice features, including baseline acoustic (BLA) parameters and Mel-Frequency Cepstral Coefficients (MFCCs), were extracted and used to train three ML classifiers: CatBoost (CB), Random Forest (RF), and Support Vector Machine (SVM). CB demonstrated the highest test accuracy at 78%. 

Study III investigates the effects of signal segmentation on model performance and shows that certain temporal segments of voice recordings contain more informative patterns, enhancing classification outcomes by increasing accuracy to 85%. Study IV applies statistical and practical significance tests to compare voice features between COPD and healthy groups. A total of 34 features, including shimmer measures and higher-order MFCC derivatives, were found to meaningfully differentiate the groups. 

This thesis reframes the human voice as a source of clinically relevant data, demonstrating how it can be digitized, analyzed, and interpreted using ML to aid COPD assessment. The results indicate that voice-based analysis can provide an accessible, non-invasive, and scalable complement to existing diagnostic tools. By integrating technical, clinical, and ethical perspectives, the thesis contributes new knowledge and practical methodologies that align with AHT's goal of creating value-driven, user-centered healthcare solutions. The findings support future development of mobile and remote voice-based screening tools for COPD and other conditions.

Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) models

This study aims to investigate the possible effects of segmentation of the utterance of vowel "a" on the performance of ML classifiers CatBoost (CB), Random Forest (RF), and Support Vector Machine (SVM). This research involves training individual ML models using three distinct dataset constructions: full-sequence, segment-wise, and group-wise, derived from the utterance of the vowel "a" which consists of 1058 recordings belonging to 48 participants. This approach comprehensively analyzes how each data categorization impacts the model's performance and results.

A nested cross-validation (nCV) approach was implemented with grid search for hyperparameter optimization. This rigorous methodology was employed to minimize overfitting risks and maximize model performance. Compared to the full-sequence dataset, the findings indicate that the second segment yielded higher results within the four-segment category. Specifically, the CB model achieved superior accuracy, attaining 97.8% and 84.6% on the validation and test sets, respectively. The same category for the CB model also demonstrated the best balance regarding true positive rate (TPR) and true negative rate (TNR), making it the most clinically effective choice.

These findings suggest that time-sensitive properties in vowel production are important for COPD classification and that segmentation can aid in capturing these properties. Despite these promising results, the dataset size and demographic homogeneity limit generalizability, highlighting areas for future research. Trial registration The study is registered on clinicaltrials.gov with ID: NCT06160674. 

Background

Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.

Objective

The proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset.

Methods

Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures.

Results

The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order.

Conclusion

This study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis.

Among the growing elderly population, the number of people with neurocognitive disease increases, highlighting the need for early diagnosis. Mini-Mental State Examination (MMSE) is one of the tools used to diagnose neurocognitive disease. The existence of a relationship between degraded oral health and decreased MMSE scores is known. Using machine Learning (ML) techniques, the present study aimed to study the potential of using oral health and demographic examination data to indicate the level of MMSE score. Data from a study evaluating oral health over time and data from an ongoing study evaluating the general health in an elderly population were used as inputs to ML models Random Forest (RF), Support Vektor Machine (SVM), and Catboost (CB) for the binary indication of MMSE score 30 or MMSE score 26 or lower was used to find the best classification performance to distinguish between MMSE low and healthy control (HC) groups. The classifiers were trained using the nested cross-validation (nCV) method to mitigate the risk of overfiting. CB and RF achieved the highest classification accuracy of 80%. However, the CB classifier outperformed other classifiers with 76.4 average accuracies over all the nCV combinations. The oral health parameters and demographics used as input to the ML classifiers carry enough information to distinguish between MMSE low and HC groups. This study suggests that oral health examination might provide information that can be used with the help of Machine Learning (ML) to indicate lowered MMSE scores.

The process of internationalising higher education brings about numerous benefits f or t eaching a nd l earning activities. It fosters an extensive exchange of information that extends beyond national borders. Nonetheless, achieving an acceptable standard of quality to meet the expectations of international students necessitates diligent feedback and inquiry to comprehend and address their experiences effectively. This work's primary objective is to investigate international students experiences, focusing on factors that can influence t h eir a c ademic pursuits, including learning activities, learning environment, academic collaboration and social integration at a higher education institute in Sweden. This study employs a quantitative analysis methodology to examine anonymous surveys completed by a group of international students at our university. The gathered data were classified i n to t h ree d i stinct c a tegories a n d subjected to separate analyses within each category. The findings suggest that the expectations of international students are met mainly regarding learning activities, receiving the highest score. However, challenges were observed in the learning environment, especially with regard to academic collaboration and social integration with domestic students. Recommendations for improvement include promoting social integration, enhancing academic collaboration, improving information dissemination, and establishing partnerships with the municipality to ensure better accommodation. These findings offer valuable insights for our university and other institutions striving to enhance international students' educational quality and overall experience. © 2024 IEEE.

Background: Advancements in machine learning (ML) techniques and voice technology offer the potential to harness voice as a new tool for developing decision-support tools in healthcare for the benefit of both healthcare providers and patients. Motivated by technological breakthroughs and the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, numerous studies aim to investigate the diagnostic potential of ML algorithms in the context of voice-affecting disorders. This thesis focuses on respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and explores the potential of a decision support tool that utilizes voice and ML. This exploration exemplifies the intricate relationship between voice and overall health through the lens of applied health technology (AHT. This interdisciplinary nature of research recognizes the need for accurate and efficient diagnostic tools.

Objective: The objectives of this licentiate thesis are twofold. Firstly, a Systematic Literature Review (SLR) thoroughly investigates the current state of ML algorithms in detecting voice-affecting disorders, pinpointing existing gaps and suggesting directions for future research. Secondly, the study focuses on respiratory health, specifically COPD, employing ML techniques with a distinct emphasis on the vowel "A". The aim is to explore hidden information that could potentially be utilized for the binary classification of COPD vs no COPD. The creation of a new Swedish COPD voice classification dataset is anticipated to enhance the experimental and exploratory dimensions of the research.

Methods: In order to have a holistic view of a research field, one of the commonly utilized methods is to scan and analyze the literature. Therefore, Paper I followed the methodology of an SLR where existing journal publications were scanned and synthesized to create a holistic view in the realm of ML techniques employed to experiment on voice-affecting disorders. Based on the results from the SLR, Paper II focused on the data collection and experimentation for the binary classification of COPD, which was one of the gaps identified in the first study. Three distinct ML algorithms were investigated on the collected datasets through voice features, which consisted of recordings collected through a mobile application from participants 18 years old and above, and the most utilized performance measures were computed for the best outcome. 

Results: The summary of findings from Paper I reveals the dominance of Support Vector Machine (SVM) classifiers in voice disorder research, with Parkinson's Disease and Alzheimer's Disease as the most studied disorders. Gaps in research include underrepresented disorders, limited datasets in terms of number of participants, and a lack of interest in longitudinal studies. Paper II demonstrates promising results in COPD classification using ML and a newly developed dataset, offering insights into potential decision support tools for COPD diagnosis.

Conclusion: The studies covered in this dissertation provide a comprehensive literature summary of ML techniques used to support decision-making on voice-affecting disorders for clinical outcomes. The findings contribute to understanding the diagnostic potential of using ML on vocal features and highlight avenues for future research and technology development. Nonetheless, the experiment reveals the potential of employing voice as a digital biomarker for COPD diagnosis using ML.

Background: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. © 2023 Journal of Medical Internet Research. All rights reserved.

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.