Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective: To address the limitation of ineffective feature selection in existing research on stroke risk detection, we propose a new feature selection method called Weighting- and Ranking-based Hybrid Feature Selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods: WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, RELIEF, and Chi-squared, and used sensitivity, specificity, accuracy, and Youden’s index as performance metrics to evaluate the proposed method. Results: This study chooses 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample includes 28 features (the 24 blood test features and the 4 demographic features). The results of evaluation show that the proposed method selects 9 important features out of the original 28 features and significantly outperforms baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieves sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), the classification accuracy of 81.5% (645/792), and Youden’s Index of 0.63 using only the top 9 features. We also present a chart for visualizing the risk of having ischemic strokes. Conclusions: This research proposes, develops, and evaluates a new feature selection method for identifying most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

By simulating the sound field of a round piston transducer with the Kirchhoff integral theorem and analyzing the shape of ultrasound beams and propagation characteristics in a metal container wall, this study presents a model for calculating the echo sound pressure by using the Kirchhoff paraxial approximation theory, based on which and according to different ultrasonic impedance between gas and liquid media, a method for detecting the liquid level from outside of sealed containers is proposed. Then, the proposed method is evaluated through two groups of experiments. In the first group, three kinds of liquid media with different ultrasonic impedance are used as detected objects; the echo sound pressure is calculated by using the proposed model under conditions of four sets of different wall thicknesses. The changing characteristics of the echo sound pressure in the entire detection process are analyzed, and the effects of different ultrasonic impedance of liquids on the echo sound pressure are compared. In the second group, taking water as an example, two transducers with different radii are selected to measure the liquid level under four sets of wall thickness. Combining with sound field characteristics, the influence of different size transducers on the pressure calculation and detection resolution are discussed and analyzed. Finally, the experimental results indicate that measurement uncertainly is better than ±5 mm, which meets the industrial inspection requirements.

Inkjet printing is regarded as a new generation of green intelligent manufacturing technology. However, precise control of the state of droplets in inkjet printing is critical and costly. In this study, a voltage-driven signal-based ink droplet state prediction model suitable for a small sample data environment is proposed. Seven public ink datasets and self-collected experimental data are grouped together, and the decision tree, AdaBoost.R2, and two-stage TrAdaBoost.R2 algorithms are used, respectively. The two-stage TrAdaBoost.R2 model shows excellent prediction performance in the case of insufficient data by transferring knowledge of different inks. In particular, it achieves mean square errors of 0.00219 for droplet volume predictions and 0.0645 for velocity predictions. Similarly, the mean absolute percentage errors are 0.91% for droplet volume and 1.37% for velocity. Furthermore, the two-stage TrAdaBoost.R2 model demonstrates strong generalization ability and adaptability to maintain high prediction accuracy under different conditions. The results indicate that the two-stage TrAdaBoost.R2 effectively mitigates the prediction performance issues caused by data scarcity, paving the way for technological advancements in the field of inkjet printing.

Based on the principle of underwater transducers, an ultrasonic four-laminated transducer with a frequency of 1 MHz was proposed to solve the problem of large energy attenuation when ultrasonic waves propagate in viscoelastic media. First, this study targeted solid rocket propellant as the research object, and the energy attenuation characteristics of ultrasonic waves propagating in viscoelastic media were analyzed through the derivation of the wave equation. Second, the structure of a four-laminated transducer with a frequency of 1 MHz was designed, and the resonance frequency was obtained by a graphical method. The sound field simulation and experimental results showed that the gain of the four-laminated transducer was 15 dB higher than that of the single-wafer transducer. An ultrasonic feature scanning system was built to complete the qualitative and quantitative detection of the smallest artificial hole ($\varphi$2 mm × 10 mm). Finally, two different natural defects were scanned, and the results were compared with those obtained using an industrial computed tomography detection system. The results showed that the ultrasonic method was more accurate in characterizing two natural defects. The primary cause was that the industrial CT was not sensitive to defects parallel to the incident direction of the ray. Therefore, this study not only achieved the qualitative and quantitative nondestructive testing of solid rocket propellants, but also provides an important reference for other viscoelastic components.

BACKGROUND Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. OBJECTIVE This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. METHODS WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. RESULTS This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. CONCLUSIONS This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.