scholarly journals A Comparative Study to Evaluate the Performance of Classification Algorithms in Mammogram Analysis

2018 ◽  
Vol 7 (3.6) ◽  
pp. 154
Author(s):  
S K. Sajan ◽  
M Germanus Alex
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Decision Tree ◽  
Automated System ◽  
Support Vector ◽  
Classification Algorithms ◽  
Neural Network Classifier ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Mammogram Image

Breast cancer is a major threat humans are facing irrespective of geographical limits. The awareness about breast cancer has increased during the last decade and many preventive measures were in practice to detect the breast cancer before the symptoms were felt. Mammography is a screening methodology currently in practice. In this paper the mammogram image is analyzed using automated system. The automated system is designed to be capable of distinguishing the mammogram image into a normal or malignant. This process involves image enhancement and image segmentation at preprocessing level. Histogram equalization technique is used to transform low contrast region of the mammogram into region with higher contrast and Fuzzy C Means (FCM) algorithm is used to segment the mammogram image into regions suitable for further analysis. After enhancement and segmentation at preprocessing level the classification is done using three classification algorithms like decision tree classifier, Neural Network classifier and Support Vector Machine (SVM). The performance of the classification algorithms is evaluated using the following criteria like speed, flexibility, robustness, scalability, interpretability, Time complexity and also based on accuracy, sensitivity and specificity. The results obtained in classification are compared with other classification algorithms. It is found that the neural network classifier approach produces better results compared to other classifiers.The average accuracy in diagnosis by Neural Network approach classifier is around 91%.  Also it is found that the decision tree approach is much flexible and easy to use compared to other approaches.  

scholarly journals Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Kaushalya Dissanayake ◽  
Md Gapar Md Johar
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Decision Tree ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Subset ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Selection Techniques

Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.

scholarly journals PERFORMANCE ANALYSIS OF BREAST CANCER CLASSIFICATION USING DECISION TREE CLASSIFIERS

2017 ◽  
Vol 9 (2) ◽  
pp. 19 ◽  
Author(s):  
P. Hamsagayathri ◽  
P. Sampath
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Ductal Carcinoma ◽  
Research Work ◽  
The United States ◽  
Breast Cancer Dataset ◽  
Decision Tree Classifier ◽  
Cancer Dataset ◽  
Term Survival ◽  
Tree Classifier

Breast cancer is one of the dangerous cancers among world’s women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in the United States. Also, 246,660 new cases of women with cancer are estimated for the year 2016. Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification plays an important role in breast cancer detection and used by researchers to analyse and classify the medical data. In this research work, priority-based decision tree classifier algorithm has been implemented for Wisconsin Breast cancer dataset. This paper analyzes the different decision tree classifier algorithms for Wisconsin original, diagnostic and prognostic dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

2020 ◽  
Vol 10 (22) ◽  
pp. 8137
Author(s):  
Sushruta Mishra ◽  
Pradeep Kumar Mallick ◽  
Hrudaya Kumar Tripathy ◽  
Akash Kumar Bhoi ◽  
Alfonso González-Briones
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Heart Disease ◽  
Chronic Disease ◽  
Decision Tree ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Accuracy Rate ◽  
Filter Methods ◽  
Tree Classifier

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

scholarly journals Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 173 ◽  
Author(s):  
Ansam Khraisat ◽  
Iqbal Gondal ◽  
Peter Vamplew ◽  
Joarder Kamruzzaman ◽  
Ammar Alazab
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
False Alarm Rates

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.

scholarly journals DECISION TREE CLASSIFIERS FOR CLASSIFICATION OF BREAST CANCER

2017 ◽  
Vol 9 (2) ◽  
pp. 31 ◽  
Author(s):  
P. Hamsagayathri ◽  
P. Sampath
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Ductal Carcinoma ◽  
Kappa Statistic ◽  
Breast Cancer Dataset ◽  
Decision Tree Classifier ◽  
Cancer Dataset ◽  
Term Survival ◽  
Time To Build ◽  
Tree Classifier

Objective: Breast cancer is one of the dangerous cancers among world’s women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. Survey: According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in united states. Also, 246,660 new cases of women with cancer are estimated for the year 2016.Methods: Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification is one of the vital techniques used by researchers to analyze and classify the medical data.Results: This paper analyzes the different decision tree classifier algorithms for seer breast cancer dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.Conclusion: The simulation results shows REPTree classifier classifies the data with 93.63% accuracy and minimum RMSE of 0.1628 REPTree algorithm consumes less time to build the model with 0.929 ROC and 0.959 PRC values. By comparing classification results, we confirm that a REPTree algorithm is better than other classification algorithms for SEER dataset.

Sign in / Sign up

Export Citation Format

Share Document