scholarly journals Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ratchadaporn Kanawong ◽  
Tayo Obafemi-Ajayi ◽  
Tao Ma ◽  
Dong Xu ◽  
Shao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Color Space ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Control Group

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Domingos S. M. Andrade ◽  
Luigi Maciel Ribeiro ◽  
Agnaldo J. Lopes ◽  
Jorge L. M. Amaral ◽  
Pedro L. Melo
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
Diagnostic Accuracy ◽  
Group Versus ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Control Group ◽  
Extreme Gradient Boosting

Abstract Introduction The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task. Methods Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB). Results and discussion The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC = 0.97). Conclusions Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

scholarly journals Toward Palmprint Recognition Methodology Based Machine Learning Techniques

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
M. M. Ata ◽  
K. M. Elgamily ◽  
M. A. Mohamed
Keyword(s):  
Machine Learning ◽  
Hough Transform ◽  
Recognition Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Palmprint Recognition ◽  
Feature Vectors ◽  
Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

scholarly journals Human Behavior Prediction and Analysis Using Machine Learning-A Review

2021 ◽  
Vol 12 (5) ◽  
pp. 870-876
Author(s):  
Monali Gulhane, T.Sajana
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Social Behavior ◽  
Human Behavior ◽  
Technical Difficulty ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Classification Techniques

Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

Classification of Sleep Apnea Using ECG Signals With Machine Learning Techniques

2021 ◽  
pp. 184-203
Author(s):  
Karthik R. ◽  
Ifrah Alam ◽  
Bandaru Umamadhuri ◽  
Bharath K. P. ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Soft Tissues ◽  
Amplitude Distribution ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Feed Forward Neural Network ◽  
Ecg Signals

In this chapter, the authors use various signal processing techniques to analyze and gain insights on how ECG signals for patients suffering from sleep apnea (sleep apnea or obstructive sleep apnea occurs when the muscles that support the soft tissues in the throat, such as tongue and soft palate, relax temporarily) disease vary with respect to a normal person's ECG. The work has three stages: firstly, to identify waves, complexes, morphology in an ECG which reflect the presence of the disease; second, feature extraction techniques to extract features of ECG such as duration of the wave, amplitude distribution, and morphology classes; and third, detailed clustering (unsupervised) algorithm analysis of the extracted features with efficient feature reduction methodologies such as PCA and LDA. Finally, the authors use supervised machine learning algorithms (SVM, naive Bayes classifier, feed forward neural network, and decision tree) to distinguish between ECG signals with sleep apnea and normal ECG signals.

Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset

2020 ◽  
Vol 10 (2) ◽  
pp. 1-26
Author(s):  
Naghmeh Moradpoor Sheykhkanloo ◽  
Adam Hall
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Third Party ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Insider Threat ◽  
Threat Detection ◽  
Imbalanced Dataset ◽  
The Impact

An insider threat can take on many forms and fall under different categories. This includes malicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learning techniques have been studied in published literature as a promising solution for such threats. However, they can be biased and/or inaccurate when the associated dataset is hugely imbalanced. Therefore, this article addresses the insider threat detection on an extremely imbalanced dataset which includes employing a popular balancing technique known as spread subsample. The results show that although balancing the dataset using this technique did not improve performance metrics, it did improve the time taken to build the model and the time taken to test the model. Additionally, the authors realised that running the chosen classifiers with parameters other than the default ones has an impact on both balanced and imbalanced scenarios, but the impact is significantly stronger when using the imbalanced dataset.

scholarly journals Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Shilun Yang ◽  
Yanjia Shen ◽  
Wendan Lu ◽  
Yinglin Yang ◽  
Haigang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Cross Validation ◽  
Bayesian Models ◽  
Machine Learning Algorithms ◽  
Therapeutic Effects ◽  
Test Set ◽  
Screening Experiments ◽  
Fold Cross Validation

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

scholarly journals Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

2021 ◽  
pp. 1-21
Author(s):  
Helper Zhou ◽  
Victor Gumbo
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Predictive Modelling ◽  
Sales Performance ◽  
Ordinary Least Squares ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Operator

The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.

scholarly journals Automated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier

2020 ◽  
Vol 6 (1) ◽  
pp. 16-30
Author(s):  
Somayeh Raiesdana ◽  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Fuzzy Inference ◽  
Brain Mri ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Inference System ◽  
Common Technique ◽  
Magnetic Resonance Imaging Mri

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentative. Objectives: To develop an automated non-subjective method for the detection and quantification of MS lesions. Materials & Methods: This paper focuses on the automatic detection and classification of MS lesions in brain MRI images. Two datasets, one simulated and the other one recorded in hospital, are utilized in this work. A novel hybrid algorithm combining image processing and machine learning techniques is implemented. To this end, first, intricate morphological patterns are extracted from MRI images via texture analysis. Then, statistical textures-based features are extracted. Afterward, two supervised machine learning algorithms, i.e., the Hidden Markov Model (HMM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed within a hybrid platform. The hybrid system makes decisions based on ensemble learning. The stacking technique is used to apply predictions from both models o train a perceptron as a decisive model. Results: Experimental results on both datasets indicate that the proposed hybrid method outperforms HMM and ANFIS classifiers with reducing false positives. Furthermore, the performance of the proposed method compared with the state-of-the-art methods, was approved. Conclusion: Remarkable results of the proposed method motivate advanced detection systems employing other MRI sequences and their combination.

scholarly journals Framework for Providing Security in Private Cloud using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7641-7645
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cyber Attack ◽  
Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

Sign in / Sign up

Export Citation Format

Share Document