My Bloooooooog--------Find Most Hot Topics Here
Acoustic Analysis and Prediction of Type 2 Diabetes Mellitus--Using Smartphone-Recorded Voice Segments
AI and acoustics are being used together in healthcare for various purposes, such as :
Diagnosing and monitoring diseases based on the sounds produced by the body. For example, AI can analyze the acoustic signals of the heart, lungs, and voice to detect abnormalities, such as arrhythmias, asthma, and COVID-191 .
Here is a example--Acoustic Analysis and Prediction of Type 2 Diabetes Mellitus Using Smartphone-Recorded Voice Segments
Math View on This Paper
1. The study utilizes common statistical tests like t-tests, p-values, and Cohen's d to identify significant vocal feature differences between diabetic and non-diabetic groups. This provides mathematical evidence of vocal changes.
2. Machine learning models including logistic regression, Naive Bayes, and support vector machines are employed. These have underlying mathematical foundations in statistics and optimization.
3. Performance metrics like accuracy, sensitivity, specificity, and ROC curves are used to evaluate and compare models. These rely on mathematical concepts like conditional probability.
4. Techniques like cross-validation and matched/unmatched training/testing splits are used to rigorously validate the models and avoid statistical issues like overfitting.
5. Calculation of the net reclassification index provides a mathematical approach to quantifying the added value of the vocal biomarkers.
6. The study is limited by the small sample size of diabetic women, resulting in high variability in some mathematical model estimates like sensitivity. Larger samples would provide more statistical power.
7. More advanced mathematical techniques could be explored in the future, like neural networks or deep learning approaches to model the vocal data.
Interesting Points:
​
I had internship this summer at Institute of Acoustics, Chinese Academy of Sciences, where I engaged in a project which is to reduce noise in earphone through ML, we adopted some math tools like above…
Elderly Care Services through Machine Learning and Factor Rotation
Factor Rotation in Machine Learning
-
Factor rotation like varimax simplifies the factor structure obtained from principal component analysis by maximizing the variance of each factor. This makes the factors more interpretable.
-
Rotated factors can be used as features for machine learning algorithms like k-means clustering and random forest classification.
-
Machine learning can identify patterns and relationships between the factors and the original variables. For example, clustering finds groups of similar observations based on factor values.
-
Classification algorithms like random forest can predict cluster membership and determine which factors are most important for the predictions.
Summary:
With the continuous intensification of China's aging population, the societal pressure for elderly care has significantly escalated. Addressing the pressing concern of providing appropriate elderly care services to the growing elderly population has become imperative. China's current elderly care service types encompass institutional care, community-based care, and in-home care. Different elderly care modalities are suitable for diverse segments of the elderly population, necessitating distinct socio-structural and human resource allocations. Institutional care requires dedicated facilities and personnel for elderly care services, whereas community-based care alleviates elderly individuals to a certain extent by offering in-home services such as sanitation and meal delivery. In-home care, on the other hand, primarily relies on the elderly person's offspring to provide necessary care.
Given the individual disparities among elderly individuals, their respective demands for elderly care services vary. Therefore, this study introduces an elderly care service selection prediction model that, by analyzing various attributes of elderly individuals, forecasts the likely choice of elderly care service type. Simultaneously, taking into consideration the conditions of institutional and community-based care across provinces and cities, as well as the fundamental characteristics of the elderly population, this study analyzes the supply and demand situation in various regions and assesses supply-demand equilibrium. Through the prediction of elderly care services and an analysis of the supply-demand situation, this study aims to assist the government in more rationally allocating elderly care resources across different regions to meet the elderly population's diverse elderly care needs.