scholarly journals Analyzing Tuberculosis Reactivation in Patients with Rheumatoid Arthritis and Ankylosing Spondylitis Treated with Biological Therapy Using Machine Learning Methods

2021 ◽  
Vol 11 (23) ◽  
pp. 11400
Author(s):  
Andra-Maria Mircea-Vicoveanu ◽  
Elena Rezuș ◽  
Florin Leon ◽  
Silvia Curteanu
Keyword(s):  
Rheumatoid Arthritis ◽  
Ankylosing Spondylitis ◽  
Nearest Neighbor ◽  
Biological Therapy ◽  
Attribute Selection ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Correlation Based Feature Selection ◽  
Tuberculosis Reactivation ◽  
Selection Algorithms

This study is based on the consideration that the patients with rheumatoid arthritis and ankylosing spondylitis undergoing biological therapy have a higher risk of developing tuberculosis. The QuantiFERON-TB Gold test result was the output of the models and a series of features related to the patients and their treatments were chosen as inputs. A distribution of patients by gender and biological therapy, followed at the time of inclusion in the study, and at the end of the study, is made for both rheumatoid arthritis and ankylosing spondylitis. A series of classification algorithms (random forest, nearest neighbor, k-nearest neighbors, C4.5 decision trees, non-nested generalized exemplars, and support vector machines) and attribute selection algorithms (ReliefF, InfoGain, and correlation-based feature selection) were successfully applied. Useful information was obtained regarding the influence of biological and classical treatments on tuberculosis risk, and most of them agreed with medical studies.

scholarly journals AB0182 EVALUATION OF THE SOCIO-PROFESSIONAL IMPACT OF ANKYLOSING SPONDYLITIS AND RHEUMATOID ARTHRITIS IN TUNISIA: DATA FROM THE BINAR REGISTRY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1390.2-1391
Author(s):  
H. Hachfi ◽  
N. Ben Chekaya ◽  
D. Khalifa ◽  
M. Brahem ◽  
H. Themri ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Ankylosing Spondylitis ◽  
Marital Status ◽  
Biological Therapy ◽  
Total Duration ◽  
National Registry ◽  
Inflammatory Rheumatic Diseases ◽  
Physically Active ◽  
Absence From Work ◽  
Professional Impact

Background:Rheumatoid arthritis (RA) and ankylosing spondylitis (AS) are disabling and common chronic inflammatory rheumatic diseases.Objectives:The aim of our study was to evaluate the socio-professional impact of RA and AS.Methods:Using the Biological National Registry (BINAR) data, which includes ten tunisian rheumatology centers,we identified patients≥18 years with AS and RA according to the ACR and EULAR 2010 criteria(RA) and ASAS 2009 (AS), receiving biotherapy for less than two years.Results:298 patients were included in the study. The percentage of patients with RA was 58.7 % and those with AS 41.3%. The sex ratio was 0.6. The average age of the onset of the disease was 49.1 years ± 14.1 years [18–79]. For marital status, 72% were married, single (25%), widowed (2.6%) and divorced (0.4%). 22.4% of patients were illiterate, 32.7 % (primary), 28.3% (secondary) and 16.6% had an university level. For the RA population, a high disease activity (DAS28-ESR >5.1) was detected in 36% of patients, an erosive arthritis in 73.1% and 7.2% had a coxitis. In the AS group, an elevate BASDAI (BASDAI≥4) was detected in 56.9% of patients and 39% had coxitis. All patients have received Biological therapy concomitant with corticosteroids (59.1%), methotrexate (42.6%), salazopirine (20.8%) and leflunomide (4.7%). 54% of patients had a comorbidity, of which 1.7% was depression. More than half of our patients (54.3%) were unemployed, 40 % were professionally active, and 5.7% were retired due to the rheumatic condition. Absence from work was observed in 15.1% of cases with a total duration exceeding three months in 55.5% of cases. 37.9 % of patients were physically active: regularly (9.8%), irregularly (28.1%) and (62.1%) were sedentary. For the functional impact, HAQ score was 1.31± 0.7 for RA and BASFI was 5.2 ± 4.8 for AS. The working abandonment is significantly associated to: marital status (p=0.039), low level of education (p=0.04),depression (p<0.001), high activity of AS (p=0.004) and BASFI>4 (p=0,001).Conclusion:RA or AS requiring biotherapy have a high socio-economic impact and are responsible for absenteeism at work and even for early working abandonment. Early therapeutic management and a global assessment are essential in order to improve quality of life and working conditions. Longitudinal studies are needed to assess the effect of biological therapy on the socio-professional impact of these chronic inflammatory rheumatic disease.Disclosure of Interests:None declared

BREAST CANCER DETECTION USING RSFS-BASED FEATURE SELECTION ALGORITHMS IN THERMAL IMAGES

2021 ◽  
pp. 2150020
Author(s):  
Nazila Darabi ◽  
Abdalhossein Rezai ◽  
Seyedeh Shahrbanoo Falahieh Hamidpour
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Vital Role ◽  
Support Vector ◽  
Computer Aided Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Common Cancer ◽  
Selection Algorithms

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.

Hybrid adapted fast correlation FCBF-support vector machine recursive feature elimination for feature selection

2020 ◽  
Vol 14 (3) ◽  
pp. 269-279
Author(s):  
Hayet Djellali ◽  
Nacira Ghoualmi-Zine ◽  
Souad Guessoum
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Svm Classifier ◽  
Hybrid Architecture ◽  
Features Selection ◽  
K Nearest Neighbors ◽  
Correlation Based Feature Selection ◽  
Embedded Method

This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC.

Feature Selection Algorithm for Hyperlipidemia Classification

2014 ◽  
Vol 701-702 ◽  
pp. 110-113
Author(s):  
Qi Rui Zhang ◽  
He Xian Wang ◽  
Jiang Wei Qin
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Information Gain ◽  
Classification Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Document Frequency ◽  
Selection Algorithms ◽  
Term Weights

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.

scholarly journals Prediction of Cancer and Suggestion of Therapies

2020 ◽  
Author(s):  
Bhavani M ◽  
Pavithra V ◽  
Monesh R
Keyword(s):  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Prediction System ◽  
Cancer Prediction ◽  
Mining Algorithm ◽  
Vector Machines ◽  
The Common ◽  
Genetic And Environmental Factors ◽  
Selection Algorithms

Cancer is becoming one among the common diseases in day to today life, determining cancer in an earlier stage is still problematic. Identification of genetic and environmental factors is necessary to predict the type of cancer. The idea is to develop a cancer prediction system that predict lung and oral cancer depending on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this study is to do a comparative analysis using different algorithms for cancer prediction and suggestion of therapy.

scholarly journals Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Kevin Christian Tanus ◽  
Raynaldy Valentino Sukiwun ◽  
Joseph Kristiano ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN  untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

scholarly journals Prognosis of Cancer and Proposition of Therapeutics

2020 ◽  
Vol 9 (8) ◽  
pp. 394-397
Keyword(s):  
Random Forest ◽  
Genetic Factors ◽  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Random Forest Algorithm ◽  
Cancer Prediction ◽  
Vector Machines ◽  
The Common ◽  
Selection Algorithms

Cancer is becoming one of the common diseases in day today life, identifying it in a prior stage is still difficult. Identification of environmental and genetic factors is necessary to predict the cancer. We developed a cancer prediction system to predict lung and oral cancer based on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this system is to predict the type of cancer and the suggested therapy using random forest algorithm.

scholarly journals The Impact of Feature Selection Methods for Classifying Arabic Textual Data

2019 ◽  
Vol 8 (4) ◽  
pp. 1333-1338
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Feature Space ◽  
Support Vector ◽  
Selection Methods ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Selection Algorithms ◽  
The Impact

Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x2 ), Information Gain (IG), and Correlation (CFS). These algorithms have been investigated widely for English text, while studies for Arabic text are still limited. In this paper, we investigated four well-known algorithms: Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree against benchmark Arabic textual datasets, called Saudi Press Agency (SPA) to evaluate the impact of feature selection methods. Using the WEKA tool, we have experimented the application of the four mentioned classification algorithms with and without feature selection algorithms. The results provided clear evidence that the three feature selection methods often improves classification accuracy by eliminating irrelevant features.

scholarly journals Improving Convolutional Neural Networks’ Accuracy in Noisy Environments Using k-Nearest Neighbors

2018 ◽  
Vol 8 (11) ◽  
pp. 2086 ◽  
Author(s):  
Antonio-Javier Gallego ◽  
Antonio Pertusa ◽  
Jorge Calvo-Zaragoza
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Neural Codes ◽  
K Nearest Neighbors ◽  
Network Topologies ◽  
Common Technique

We present a hybrid approach to improve the accuracy of Convolutional Neural Networks (CNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (kNN) algorithm for inference. Although this is a common technique in transfer learning, we apply it to the same domain for which the network was trained. Previous works show that neural codes (neuron activations of the last hidden layers) can benefit from the inclusion of classifiers such as support vector machines or random forests. In this work, our proposed hybrid CNN + kNN architecture is evaluated using several image datasets, network topologies and label noise levels. The results show significant accuracy improvements in the inference stage with respect to the standard CNN with noisy labels, especially with relatively large datasets such as CIFAR100. We also verify that applying the ℓ 2 norm on neural codes is statistically beneficial for this approach.

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