A decision-tree model of career choice for veterinarians in clinical residency programs

2022 ◽  
pp. 1-10
Author(s):  
Martin O. Furr ◽  
Brandon M. Raczkoski
Keyword(s):  
Principal Component Analysis ◽  
Clinical Practice ◽  
Decision Tree ◽  
Career Choice ◽  
Residency Training ◽  
Academic Medicine ◽  
Training Programs ◽  
Principal Component ◽  
Component Analysis ◽  
Residency Programs

Abstract OBJECTIVE To identify factors that individuals in clinical residency training programs consider when making a choice for or against a career in academic clinical medicine. SAMPLE 207 veterinarians in clinical residency programs. PROCEDURES An online survey was distributed to 1,053 veterinarians participating in clinical residency training programs overseen by organizations recognized by the AVMA American Board of Veterinary Specialties. Results were compiled and decision factors were analyzed by means of principal component analysis to identify latent factors from the set of survey items. These factors were then used to construct a decision tree to predict respondents’ choice of whether to enter academic medicine or private clinical practice. RESULTS 207 (20%) responses were analyzed. Ninety-three of 194 (48%) respondents reported a desire to pursue a career in academic medicine, and 101 (52%) reported a desire to pursue a career in private clinical practice. Principal component analysis identified 14 items clustered on research, clinical teaching, classroom teaching, and clinical practice. A decision tree was constructed that resulted in an overall accuracy of 82% in predicting a resident's career choice of academic medicine versus private clinical practice. The construct of professional benefits had a negative effect on desiring a career in academic medicine, whereas the construct of professional priorities and having had a positive residency training experience had a positive effect on desiring a career in academic medicine. CLINICAL RELEVANCE Understanding factors that attract and encourage residents who might have an aptitude and interest in academic medicine holds important implications for addressing the shortage of veterinarians entering academic medicine.

Data Mining in Analysis of Biomechanical Signals

2009 ◽  
Vol 147-149 ◽  
pp. 588-593 ◽  
Author(s):  
Marcin Derlatka ◽  
Jolanta Pauk
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Cerebral Palsy ◽  
Spina Bifida ◽  
Decision Tree ◽  
Principal Component ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

In the paper the procedure of processing biomechanical data has been proposed. It consists of selecting proper noiseless data, preprocessing data by means of model’s identification and Kernel Principal Component Analysis and next classification using decision tree. The obtained results of classification into groups (normal and two selected pathology of gait: Spina Bifida and Cerebral Palsy) were very good.

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

scholarly journals A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

2020 ◽  
Vol 27 (4) ◽  
pp. 1-16
Author(s):  
Meenal Jain ◽  
Gagandeep Kaur
Keyword(s):  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Support Vector ◽  
Detection Techniques ◽  
Difference Measure ◽  
Reduction Techniques

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.

scholarly journals Penerapan Principal Component Analysis untuk Peningkatan Kinerja Algoritma Decision Tree pada Iris Dataset

2020 ◽  
Vol 1 (2) ◽  
pp. 55-58
Author(s):  
Putri Kurnia Handayani
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Decision Tree ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis

Data mining merupakan salah bidang ilmu yang bermanfaat untuk pengenalan pola/knowledge yang tersimpan dalam database. Klasifikasi merupakan salah satu peran dalam bidang data mining. Termasuk ke dalam supervised learning, klasifikasi digunakan untuk memprediksi objek yang belum memiliki kelas/label. Penggunaan algoritma decision tree untuk proses mining dataset bunga iris dikarenakan kemudahan dalam representasi knowledge yang dihasilkan. Selain itu, decision tree juga termasuk ke dalam eager learner sehingga akurasi dari knowledge yang dihasilkan lebih baik. Penggunaan principal component analysis (PCA) dalam optimasi algoritma decision tree, dilakukan saat preprocessing dataset. PCA berfungsi untuk mereduksi dimensi, fitur yang saling berkorelasi akan dipertahankan. Penggunaan dataset publik bunga iris diambil dari UCI Repository. Berdasarkan hasil perhitungan, akurasi algoritma decision tree setelah dilakukan optimasi dengan PCA terhadap dataset bunga iris sebesar 95.33%.

Coke Index Analysis Based on Principal Component Analysis and Decision Tree Mining

2011 ◽  
Vol 48-49 ◽  
pp. 318-322 ◽  
Author(s):  
Hong Wei Guo ◽  
Bu Xin Su ◽  
Jian Chang ◽  
Jian Liang Zhang ◽  
Wei Chao Cao
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Decision Tree ◽  
Coke Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Production Index ◽  
Evaluation Indexes ◽  
Furnace Production ◽  
Blast Furnace Production

Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision tree mining to analyze the relations between blast furnace production index and coke index . The materials of blast furnace production mainly include ore, coke and coal, in which the coke quality index have the biggest influence on the blast furnace production index. It has profound meaning to analyze the relation between coke index and blast furnace production index to evaluate Coke quality indicators reasonably[1] and improve the blast furnace production index. Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method[2]，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision-tree-based data-mining to analyze the relations between blast furnace production index and coke index. On the one hand this method can get few representative indexes from so many evaluation indexes by principal component analysis; on the other hand, decision-tree-based data-mining on the coke representative index based on the principal component analysis can get accurately quantitative relation between blast furnace production index and coke index.

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