A Hybrid Model for Enhanced Prediction of Medical Diagnosis Based on Discriminative Rule Framing and Correlated Framework Approaches

2015 ◽  
Vol 23 (01) ◽  
pp. 33-48
Author(s):  
B. Gomathy ◽  
S. M. Ramesh ◽  
A. Shanmugam
Keyword(s):  
Medical Diagnosis ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Linear Discriminant ◽  
Medical Disease ◽  
Input Dataset ◽  
Wrong Diagnosis ◽  
The Common ◽  
Independent Features ◽  
Better Than

Medical diagnosis is mostly done by experienced doctors. However, still some of the cases reported of wrong diagnosis and treatment. Patients are needed to take number of clinical tests for disease diagnosis. Most of the cases, all the tests are not contributing towards efficient diagnosis. The medical data are multidimensional and composed of thousands of independent features. So, the multidimensional database need to be analyzed and preprocessed for valuable decision making for medical diagnosis. The aim of this work is to accurately predict the medical disease with a condensed number of attributes. In this approach, the raw input dataset is preprocessed based on the common normalization approach. An association rule is used to find out the frequent used patterns to prune the dataset. Further, base rule can be applied to the pruned dataset. The Payoff and Heuristic rate can be evaluated to predict the risk analysis. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) approaches are used for better feature selection. Classification result is acquired based on minimum and maximum of residual support values. The experimental results show that the proposed scheme, can perform better than the existing algorithms to diagnose the medical disease.

scholarly journals Morphological evidence for introgressive hybridization between Feirana quadranus and Feirana taihangnica in Qinling Mountains, China

2016 ◽  
Author(s):  
Yang Song ◽  
Xin Sui ◽  
Yuhong Bian ◽  
Junfang Zhang ◽  
Junqiang Zheng ◽  
...  
Keyword(s):  
Introgressive Hybridization ◽  
Principal Component ◽  
Morphological Evidence ◽  
Qinling Mountains ◽  
Breeding Behavior ◽  
Linear Discriminant ◽  
Morphological Variations ◽  
Skin Coloration ◽  
Clear Differentiation ◽  
Better Than

Background. Feirana quadranus and Feirana taihangnica, two species of frogs inhabiting in waterbodies in the Qinling Mountains, China, are believed to be sister species that diverged 4.6 million years ago. In their sympatric area, morphological variations found between the two species imply that the two species had inter-bred. Additionally, F. taihangnica’s polyandrous breeding behavior, without amplexus, would not hinder the potential hybridization. Methods. To verify the hybridization, 117 specimens of F. quadranus and F. taihangnica were collected from eight sampling sites in their sympatric area, and 110 of the specimens were classified morphologically into VV, vw&wv, and ww, representing the putative parental and suspected hybrid types. Their maternal bloodlines were identified using a phylogenetic tree based on a region of the mitochondrial 16S rRNA gene. In total, 34 morphometric indices were selected to analyze the morphological variation between 16S-types or among morphotypes. A principal component analysis (PCA) and linear discriminant analysis (LDA) were conducted on total or partial indices for females, males, and total specimens, as well as simulated populations with falsified morphotypes. The most important indices for differentiation among morphotypes were revealed with the assistance of heat-maps. Results. In the mitochondrial DNA tree, most of the VV were in the same clade as the reference F. quadranus, labeled as Q, while most of the ww and vw&wv were grouped with the reference F. taihangnica, labeled as T. According to the PCA, there was a clear differentiation between VV and ww, while vw&wv specimens were in the middle area close to ww. According to the LDA, VV, vw&wv, and ww were clustered into three separate groups. An ambiguous differentiation between Q and T was shown both in mtDNA tree and in multivariate analyses. Seven of the specimens with conflicting classifications blurred the morphological boundary between Q and T. In both the PCA and LDA, indices that were based on the extent of bumps and skin coloration discriminated VV, vw&wv, and ww better than ratio indices that were derived from measurements. Discussion. The distribution of VV, vw&wv, and ww in multivariate spaces, especially vw&wv being scattered between VV and ww, demonstrated an introgressive hybridization pattern. The extents of bumps in the shape of an inverted "V" between the shoulder blades, spot pattern on the back, and large bumps above the anal region were the most important characteristics for differentiating between three morphotypes or between F. quadranus and F. taihangnica.

SURFACE MORPHOLOGY OF CHALKBOARD TIPS CAPTURES THE UNIQUENESS OF THE USER'S HAND STROKES

2010 ◽  
Vol 21 (04) ◽  
pp. 535-548
Author(s):  
CHRISTOPHER MONTEROLA ◽  
IRENE CRISOLOGO ◽  
JERIC TUGAFF ◽  
RENE BATAC ◽  
ANTHONY LONGJAS
Keyword(s):  
Three Dimensional ◽  
Principal Component ◽  
Photometric Stereo ◽  
Linear Discriminant ◽  
Average Classification Accuracy ◽  
Test Sets ◽  
Classification And Regression ◽  
High Degree ◽  
Dimensional Surface ◽  
Better Than

Penmanship has a high degree of uniqueness as exemplified by the standard use of hand signature as identifier in contract validations and property ownerships. In this work, we demonstrate that the distinctiveness of one's writing patterns is possibly embedded in the molding of chalk tips. Using conventional photometric stereo method, the three-dimensional surface features of blackboard chalk tips used in Math and Physics lectures are microscopically resolved. Principal component analysis (PCA) and neural networks (NN) are then combined in identifying the chalk user based on the extracted topography. We show that NN approach applied to eight lecturers allow average classification accuracy (Φ NN ) equal to 100% and 71.5 ± 2.7% for the training and test sets, respectively. Test sets are chalks not seen previously by the trained NN and represent 25% or 93 of the 368 chalk samples used. We note that the NN test set prediction is more than five-fold higher than the proportional chance criterion (PCC, Φ PCC = 12.9%), strongly hinting to a high degree of unique correlation between the user's hand strokes and the chalk tip features. The result of NN is also about three-fold better than the standard methods of linear discriminant analysis (LDA, Φ LDA = 27.0 ± 4.2%) or classification and regression trees (CART, Φ CART = 17.3 ± 3.7%). While the procedure discussed is far from becoming a practical biometric tool, our work offers a fundamental perspective to the extent on which the uniqueness of hand strokes of humans can be exhibited.

scholarly journals Morphological evidence for introgressive hybridization between Feirana quadranus and Feirana taihangnica in Tsinling Mountains, China

2016 ◽  
Author(s):  
Yang Song ◽  
Xin Sui ◽  
Yuhong Bian ◽  
Junfang Zhang ◽  
Junqiang Zheng ◽  
...  
Keyword(s):  
Introgressive Hybridization ◽  
Principal Component ◽  
Morphological Evidence ◽  
Breeding Behavior ◽  
Spot Pattern ◽  
Linear Discriminant ◽  
Morphological Variations ◽  
Skin Coloration ◽  
Clear Differentiation ◽  
Better Than

Background. Feirana quadranus and Feirana taihangnica, two species of frogs inhabiting in waterbodies in the Tsinling Mountains, China, are believed to be sister species that diverged 46,000 years ago. In their sympatric area, morphological variations found between the two species imply that the two species had inter-bred. Additionally, F. taihangnica’s polyandrous breeding behavior, without amplexus, would not hinder the potential hybridization. Methods. To verify the hybridization, 117 specimens of F. quadranus and F. taihangnica were collected from eight sampling sites in their sympatric area, and 110 of the specimens were classified morphologically into VV, vw&wv, and ww, representing the putative parental and suspected hybrid types. Their maternal bloodlines were identified using a phylogenetic tree based on a region of the mitochondrial 16S rRNA gene. In total, 34 morphometric indices were selected to analyze the morphological variation between 16S-types or among morphotypes. A principal component analysis (PCA) and linear discriminant analysis (LDA) were conducted on total or partial indices for females, males, and total specimens, as well as simulated populations with falsified morphotypes. The most important indices for differentiation among morphotypes were revealed with the assistance of heat-maps. Results. In the mitochondrial DNA tree, most of the VV were in the same clade as the reference F. quadranus, labeled as Q, while most of the ww and vw&wv were grouped with the reference F. taihangnica, labeled as T. According to the PCA, there was a clear differentiation between VV and ww, while vw&wv specimens were in the middle area close to ww. According to the LDA, VV, vw&wv, and ww were clustered into three separate groups. An ambiguous differentiation between Q and T was shown both in mtDNA tree and in multivariate analyses. Seven of the specimens with conflicting classifications blurred the morphological boundary between Q and T. In both the PCA and LDA, indices that were based on the extent of bumps and skin coloration discriminated VV, vw&wv, and ww better than ratio indices that were derived from measurements. Discussion. The distribution of VV, vw&wv, and ww in multivariate spaces, especially vw&wv being scattered between VV and ww, demonstrated an introgressive hybridization pattern. The extents of bumps in the shape of an inverted "V" between the shoulder blades, spot pattern on the back, and large bumps above the anal region were the most important characteristics for differentiating between three morphotypes or between F. quadranus and F. taihangnica.

scholarly journals Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Liwen Huang
Keyword(s):  
Discriminant Analysis ◽  
Real Data ◽  
Classification Algorithms ◽  
Data Sets ◽  
Analysis Method ◽  
Linear Discriminant ◽  
Discriminant Model ◽  
Discriminant Models ◽  
The Common ◽  
Better Than

This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models.

scholarly journals Morphological evidence for introgressive hybridization between Feirana quadranus and Feirana taihangnica in Qinling Mountains, China

2016 ◽  
Author(s):  
Yang Song ◽  
Xin Sui ◽  
Yuhong Bian ◽  
Junfang Zhang ◽  
Junqiang Zheng ◽  
...  
Keyword(s):  
Introgressive Hybridization ◽  
Principal Component ◽  
Morphological Evidence ◽  
Qinling Mountains ◽  
Breeding Behavior ◽  
Linear Discriminant ◽  
Morphological Variations ◽  
Skin Coloration ◽  
Clear Differentiation ◽  
Better Than

Background. Feirana quadranus and Feirana taihangnica, two species of frogs inhabiting in waterbodies in the Qinling Mountains, China, are believed to be sister species that diverged 4.6 million years ago. In their sympatric area, morphological variations found between the two species imply that the two species had inter-bred. Additionally, F. taihangnica’s polyandrous breeding behavior, without amplexus, would not hinder the potential hybridization. Methods. To verify the hybridization, 117 specimens of F. quadranus and F. taihangnica were collected from eight sampling sites in their sympatric area, and 110 of the specimens were classified morphologically into VV, vw&wv, and ww, representing the putative parental and suspected hybrid types. Their maternal bloodlines were identified using a phylogenetic tree based on a region of the mitochondrial 16S rRNA gene. In total, 34 morphometric indices were selected to analyze the morphological variation between 16S-types or among morphotypes. A principal component analysis (PCA) and linear discriminant analysis (LDA) were conducted on total or partial indices for females, males, and total specimens, as well as simulated populations with falsified morphotypes. The most important indices for differentiation among morphotypes were revealed with the assistance of heat-maps. Results. In the mitochondrial DNA tree, most of the VV were in the same clade as the reference F. quadranus, labeled as Q, while most of the ww and vw&wv were grouped with the reference F. taihangnica, labeled as T. According to the PCA, there was a clear differentiation between VV and ww, while vw&wv specimens were in the middle area close to ww. According to the LDA, VV, vw&wv, and ww were clustered into three separate groups. An ambiguous differentiation between Q and T was shown both in mtDNA tree and in multivariate analyses. Seven of the specimens with conflicting classifications blurred the morphological boundary between Q and T. In both the PCA and LDA, indices that were based on the extent of bumps and skin coloration discriminated VV, vw&wv, and ww better than ratio indices that were derived from measurements. Discussion. The distribution of VV, vw&wv, and ww in multivariate spaces, especially vw&wv being scattered between VV and ww, demonstrated an introgressive hybridization pattern. The extents of bumps in the shape of an inverted "V" between the shoulder blades, spot pattern on the back, and large bumps above the anal region were the most important characteristics for differentiating between three morphotypes or between F. quadranus and F. taihangnica.

Scanning Secondary Electron Microscopy of Myofibrils Using Transmission Techniques

1975 ◽  
Vol 33 ◽  
pp. 556-557
Author(s):  
M. K. Lamvik
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Carbon Films ◽  
Photographic Recording ◽  
Transmission Electron ◽  
Thin Carbon ◽  
The Common ◽  
Scanning Electron ◽  
Better Than ◽  
Cellular Organelles

When observing small objects such as cellular organelles by scanning electron microscopy, it is often valuable to use the techniques of transmission electron microscopy. The common practice of mounting and coating for SEM may not always be necessary. These possibilities are illustrated using vertebrate skeletal muscle myofibrils.Micrographs for this study were made using a Hitachi HFS-2 scanning electron microscope, with photographic recording usually done at 60 seconds per frame. The instrument was operated at 25 kV, with a specimen chamber vacuum usually better than 10-7 torr. Myofibrils were obtained from rabbit back muscle using the method of Zak et al. To show the component filaments of this contractile organelle, the myofibrils were partially disrupted by agitation in a relaxing medium. A brief centrifugation was done to clear the solution of most of the undisrupted myofibrils before a drop was placed on the grid. Standard 3 mm transmission electron microscope grids covered with thin carbon films were used in this study.

scholarly journals Deep learning for intelligent diagnosis in thyroid scintigraphy

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098284
Author(s):  
Tingting Qiao ◽  
Simin Liu ◽  
Zhijun Cui ◽  
Xiaqing Yu ◽  
Haidong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Kappa Coefficient ◽  
Diagnostic Assessment ◽  
First Year ◽  
Thyroid Scintigraphy ◽  
Diagnostic Ability ◽  
The Third ◽  
Classification Time ◽  
Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

Sign in / Sign up

Export Citation Format

Share Document