Employing feature engineering strategies to improve the performance of machine learning algorithms on echocardiogram dataset.

Objectives: This study mainly uses machine learning (ML) to make predictions by inputting features during training and inference. The method of feature selection is an important factor affecting the accuracy of ML models, and the process includes data extraction, which is the collection of all data required for ML. It also needs to import the concept of feature engineering, namely, this study needs to label the raw data of the cardiac ultrasound dataset with one or more meaningful and informative labels so that the ML model can learn from it and predict more accurate target values. Therefore, this study will enhance the strategies of feature selection methods from the raw dataset, as well as the issue of data scrubbing. Methods: In this study, the ultrasound dataset was cleaned and critical features were selected through data standardization, normalization, and missing features imputation in the field of feature engineering. The aim of data scrubbing was to retain and select critical features of the echocardiogram dataset while making the prediction of the ML algorithm more accurate. Results: This paper mainly utilizes commonly used methods in feature engineering and finally selects four important feature values. With the ML algorithms available on the Azure platform, namely, Random Forest and CatBoost, a Voting Ensemble method is used as the training algorithm, and this study also uses visual tools to gain a clearer understanding of the raw data and to improve the accuracy of the predictive model. Conclusion: This paper emphasizes feature engineering, specifically on the cleaning and analysis of missing values in the raw dataset of echocardiography and the identification of important critical features in the raw dataset. The Azure platform is used to predict patients with a history of heart disease (individuals who have been under surveillance in the past three years and those who haven't). Through data scrubbing and preprocessing methods in feature engineering, the model can more accurately predict the future occurrence of heart disease in patients.

Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments.

Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.

Classification of Dementia Severity in Taiwan Based on History-Based Clinical Diagnosis System.

BACKGROUND/AIMS: This study used HAICDDS screening questionnaire to classify the severity of dementia in Taiwan based on the clinical dementia rating scale. METHODS: LDA was applied to 6,328 Taiwanese clinical patients for classification purposes. Clustering method was used to identify the associated influential symptoms for each severity level. RESULT: LDA shows only 36 HAICDDS questions are significant to distinguish the 5 severity levels with 80% overall accuracy and it increased to 85.83% when combining normal and MCI groups. Severe dementia patients have the most serious declination in most cognitive and functionality domains, follows by moderate dementia, mild dementia, MCI and normal patients. CONCLUSION: HAICDDS is a reliable and time-saved diagnosis tool in classifying the severity of dementia before undergoing a more in-depth clinical examination. The modified CDR may be indicated for epidemiological study and provide a solid foundation to develop a machine-learning derived screening instrument to detect dementia symptoms.

CUX2, BRAP and ALDH2 are associated with metabolic traits in people with excessive alcohol consumption.

Molecular mechanisms that prompt or mitigate excessive alcohol consumption could be partly explained by metabolic shifts. This genome-wide association study aims to identify the susceptibility gene loci for excessive alcohol consumption by jointly measuring weekly alcohol consumption and γ-GT levels. We analysed the Taiwan Biobank data of 18,363 Taiwanese people, including 1945 with excessive alcohol use. We found that one or two copies of the G allele in rs671 (ALDH2) increased the risk of excessive alcohol consumption, while one or two copies of the C allele in rs3782886 (BRAP) reduced the risk of excessive alcohol consumption. To minimize the influence of extensive regional linkage disequilibrium, we used the ridge regression. The ridge coefficients of rs7398833, rs671 and rs3782886 were unchanged across different values of the shrinkage parameter. The three variants corresponded to posttranscriptional activity, including cut-like homeobox 2 (a protein coded by CUX2), Glu504Lys of acetaldehyde dehydrogenase 2 (a protein encoded by ALDH2) and Glu4Gly of BRCA1-associated protein (a protein encoded by BRAP). We found that Glu504Lys of ALDH2 and Glu4Gly of BRAP are involved in the negative regulation of excessive alcohol consumption. The mechanism underlying the γ-GT-catalytic metabolic reaction in excessive alcohol consumption is associated with ALDH2, BRAP and CUX2. Further study is needed to clarify the roles of ALDH2, BRAP and CUX2 in the liver-brain endocrine axis connecting metabolic shifts with excessive alcohol consumption.

Correction: Staining Pattern Classification of Antinuclear Autoantibodies Based on Block Segmentation in Indirect Immunofluorescence Images.

[This corrects the article DOI: 10.1371/journal.pone.0113132.].

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Staining pattern classification of antinuclear autoantibodies based on block segmentation in indirect immunofluorescence images.

Indirect immunofluorescence based on HEp-2 cell substrate is the most commonly used staining method for antinuclear autoantibodies associated with different types of autoimmune pathologies. The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research. Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance. Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.

Hiding patients confidential datainthe ECG signal viaa transform-domain quantization scheme.

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

Wavelet-based watermarking and compression for ECG signals with verification evaluation.

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user's data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis.

Sperm morphology is an important technique in identifying the health of sperms. In this paper we present a new system and novel approaches to classify different kinds of sperm images in order to assess their health. Our approach mainly relies on a one-dimensional feature which is extracted from the sperm's contour with gray level information. Our approach can handle rotation and scaling of the image. Moreover, it is fused with SVM classification to improve its accuracy. In our evaluation, our method has better performance than the existing approaches to sperm classification.

A new noninvasive approach to detect the artery's pulsating properties via tissue control method.

This paper presents a new approach, named as Tissue Control Method, to obtain the pulsating properties for artery. These properties are the variations of blood vessel diameter, instantaneous intra-arterial blood pressure, and the dynamic impedances of blood vessel. By maintaining the stiffness of tissue and tracking the variations of blood pressure at a critical location, it decouples the effect of the arterial pulsation from the tissue and measures the variations of the blood vessel diameter. Then employ the identification technique to obtain the mechanical impedances of the arterial blood vessel, and subsequently estimate the instantaneous blood pressure in the artery. The results of animal experiments show that the Tissue Control Method is feasible to detect the pulsating properties of the artery.

On measuring the instantaneous blood pressure in an artery via the tissue control method.

This paper presents a non-invasive approach to measuring the instantaneous arterial blood pressure based upon the tissue control method. According to animal experiments, there exists a pressed location where the mean blood pressure in the artery is equivalent to the mean pressure measured on the skin. At this location, the stiffness measured on the skin is equivalent to the stiffness of the tissue only. By means of the tissue control method, the pulsations of the artery are excluded from tissues. The displacement measured on the skin and the tracking pressure obtained from the controller identify the dynamical impedance of the blood vessel, and then estimate the instantaneous intra-arterial blood pressure. The experimental results show that errors of mean blood pressure, systolic blood pressure and diastolic blood pressure are less than 4%, and indicate that the tissue control method is a feasible way to detect the instantaneous arterial blood pressure.

Conceptual design of a multi-functional sphygmus detection system.

From the energy point of view, obtaining simultaneous blood pressure and flow rate of the radial artery at the wrist is very important for sphygmic diagnosis in medicine. This work depicts the conceptual design for a noninvasive multi-functional sphygmus detection system. According to Y. C. Fung's flow rate equation, flow rate is a function of the diameter of the blood vessel, two adjacent pressures, arterial compliance, the distance between the two adjacent pressures and the viscosity of the blood. Because the distance between the two adjacent pressures is preset and the viscosity is obtained from either a data bank or viscometer, four measuring techniques are proposed to realize the calculation of the flow rate. First, use thermal array sensors and a thermal image identification technique to locate the artery for positioning the sphygmus detection head at wrist and to estimate the diameter of blood vessel as well. Second, detect the delay time between the two adjacent pressures, and then compute the downstream pressure in accordance with the sampled upstream pressure and delay time. Third, employ pressure feedback control to determine the variation of the vessel diameter that in turn can be used to compute the dynamic arterial compliance. Finally, use the nonlinear flow rate equation to calculate the blood flow rate.