scholarly journals Structural Feature Extraction to recognize some of the Offline isolated Handwritten Gujarati Characters using Decision Tree Classifier

2014 ◽  
Vol 99 (15) ◽  
pp. 46-50
Author(s):  
Hetal R.Thaker ◽  
C. K. Kumbharana
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
Structural Feature ◽  
Decision Tree Classifier ◽  
Tree Classifier

Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

2021 ◽  
pp. 234-259
Author(s):  
Nitika Kapoor ◽  
Parminder Singh
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Forest Classification ◽  
Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

scholarly journals A Novel Machine Learning Approach for Tuberculosis Segmentation and Prediction Using Chest-X-Ray (CXR) Images

2021 ◽  
Vol 11 (19) ◽  
pp. 9057
Author(s):  
Xavier Alphonse Inbaraj ◽  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
High Morbidity ◽  
Decision Tree Classifier ◽  
X Ray ◽  
Poor Countries ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Novel Method

Tuberculosis is a potential fatal disease with high morbidity and mortality rates. Tuberculosis death rates are rising, posing a serious health threat in several poor countries around the world. To address this issue, we proposed a novel method for detecting tuberculosis in chest X-ray (CXR) images that uses a three-phased approach to distinguish tuberculosis such as segmentation, feature extraction, and classification. In a CXR, we utilized the Weiner filter to distinguish and reduce the impulse noise. The features were extracted from CXR images and trained using a decision tree classifier known as the stacked loopy decision tree (SLDT) classifier. For the classification process, the ROI-based morphological approach was applied in the mentioned three-phased approach, and the feature extraction was accomplished through chromatic and Prewitt-edge highlights.

scholarly journals Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

2021 ◽  
Vol 12 (2) ◽  
pp. 1147-1153
Author(s):  
T. Sathya Priya, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
In The Beginning ◽  
Sensitivity Specificity ◽  
Mammogram Images

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.  

scholarly journals Heart Disease Prediction Method using Hybrid Classifier

2019 ◽  
Vol 8 (9S4) ◽  
pp. 57-61
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Research Work ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Tree Classifier

The data mining is the approach which can extract useful information from the data. The following research work that has been described is related to the heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. For the heart disease prediction the classifier that is designed in this research work is hybrid classifier. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps which are data pre-processing, feature extraction and classification. In this paper, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. We have proposed a hybrid model that has been implemented in python. Moreover, the results are compared with Support Vector Machine (SVM) and K-Nearest Neighbor classifier (KNN).

scholarly journals Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram.

2021 ◽  
Vol 9 (5) ◽  
pp. 259-262
Author(s):  
Bhavya Rudraiah ◽  
Geetha K S
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
Performance Parameters ◽  
Decision Tree Classifier ◽  
Multiple Objects ◽  
Detection And Tracking ◽  
Tree Classifier ◽  
Multiple Detection ◽  
Cluttered Background ◽  
Histogram Feature

Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and comparison of the proposed method with various other techniques is performed using performance parameters. Results depict that the proposed technique outperforms with increased accuracy and is the top performer

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