Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.

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Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm

Kurdistan Journal of Applied Research ◽

10.24017/science.2019.2.11 ◽

2019 ◽

Vol 4 (2) ◽

pp. 110-120

Author(s):

Diyari Jalal Mussa ◽

Noor Ghazi M. Jameel

Keyword(s):

Feature Selection ◽

Structural Features ◽

Selection Method ◽

Web Pages ◽

Good Representation ◽

Forward Selection ◽

Wrapper Approach ◽

And Performance ◽

Sequential Forward Selection ◽

Selection Algorithms

In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were extracted from Tiago’s dataset, which is the most frequently used dataset by researchers. For selecting the optimal relevant features, two different types of wrapper feature selection algorithms were used in order to reduce and select best relevant features. The accuracy and performance obtained by the selected features via sequential backward selection method was better comparing to sequential forward selection method. The extracted nine optimal features can be a good representation of a spam SMS message. Additionally, the classification accuracy obtained by the proposed method using nine optimal features with XGBoost algorithm is 98.64 using 10-fold cross validation.

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Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

Symmetry ◽

10.3390/sym12030482 ◽

2020 ◽

Vol 12 (3) ◽

pp. 482

Author(s):

Manuel Torres-Vásquez ◽

Oscar Chávez-Bosquez ◽

Betania Hernández-Ocaña ◽

José Hernández-Torruco

Keyword(s):

Predictive Models ◽

Real Data ◽

Machine Learning Algorithms ◽

Guillain Barre Syndrome ◽

Minority Class ◽

Original Dataset ◽

Appropriate Treatment ◽

Binary Approach ◽

Guillain Barré Syndrome ◽

Guillain Barré

Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.

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Feature Selection and Analysis EEG Signals with Sequential Forward Selection Algorithm and Different Classifiers

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302482 ◽

2020 ◽

Author(s):

Sule Bekiryazici ◽

Ahmet Demir ◽

Gunes Yilmaz

Keyword(s):

Feature Selection ◽

Selection Algorithm ◽

Forward Selection ◽

Eeg Signals ◽

Sequential Forward Selection

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Predicting breast cancer survivability based on machine learning and features selection algorithms: a comparative study

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02590-y ◽

2020 ◽

Author(s):

Sahar A. El_Rahman

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Comparative Study ◽

Features Selection ◽

Selection Algorithms

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Greedy forward selection algorithms to Sparse Gaussian Process Regression

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.246674 ◽

2006 ◽

Author(s):

Ping Sun ◽

Xin Yao

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Forward Selection ◽

Selection Algorithms

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Computer Vision Based Detection and Quantification of Extraneous Water in Raw Milk

10.21203/rs.3.rs-625039/v1 ◽

2021 ◽

Author(s):

Bezuayehu Gutema Asefa ◽

Legesse Hagos ◽

Tamirat Kore ◽

Shimelis Admassu Emire

Keyword(s):

Machine Learning ◽

Digital Image Analysis ◽

Raw Milk ◽

Classification Performance ◽

Machine Learning Algorithms ◽

Machine Learning Technique ◽

Milk Adulteration ◽

Learning Technique ◽

Total Accuracy

Abstract A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.

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DDoS Detection on Internet of Things using Unsupervised Algorithms

E3S Web of Conferences ◽

10.1051/e3sconf/202129701005 ◽

2021 ◽

Vol 297 ◽

pp. 01005

Author(s):

Hailyie Tekleselassie

Keyword(s):

Learning Algorithms ◽

Classification Performance ◽

Machine Learning Algorithms ◽

Transport Layer ◽

Accuracy Score ◽

Ddos Attacks ◽

Knowledge Based ◽

Ddos Detection ◽

Iot Devices ◽

Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

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A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Journal of Clinical Medicine ◽

10.3390/jcm7080223 ◽

2018 ◽

Vol 7 (8) ◽

pp. 223 ◽

Cited By ~ 4

Author(s):

Zhidong Zhao ◽

Yang Zhang ◽

Yanjun Deng

Keyword(s):

Heart Rate ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Principal Component ◽

Machine Learning Algorithms ◽

Medical Decision ◽

Support Vector ◽

Visual Interpretation ◽

Adaptive Boosting ◽

Original Dataset

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

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