Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

2019 ◽  
Vol 58 (06) ◽  
pp. 205-212
Author(s):  
Cirruse Salehnasab ◽  
Abbas Hajifathali ◽  
Farkhondeh Asadi ◽  
Elham Roshandel ◽  
Alireza Kazemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Review ◽  
Predictor Variables ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors ◽  
Hematopoietic Stem ◽  
Machine Learning Classification ◽  
Graft Versus Host ◽  
Meta Analyses

Abstract Background The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged. Objective This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used. Methods A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered. Results After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables. Conclusion Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.

scholarly journals SENTIMENT ANALYSIS OF COVID-19 TWEETS

2021 ◽  
Vol 5 (1) ◽  
pp. 566-576
Author(s):  
Azeez A. Nureni ◽  
Victor E. Ogunlusi ◽  
Emmanuel Junior Uloko
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Global Pandemic ◽  
Machine Learning Approach

Sentiment analysis involves techniques used in analyzing texts in order to identify the sentiment and emotion dominant in such texts and classify them accordingly. Techniques involved include but not limited to preprocessing of texts and the use a machine learning or lexical based approach in classifying these texts. In this research, attempt was made to adopt a machine learning approach to classify tweets on Covid-19 which is considered a global pandemic. To achieve this noble objective, a cross-dataset approach was applied to train four machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), as well as K-Nearest Neighbors algorithm (KNN). The final result will not only assist us in knowing the best performing algorithm, it will also assist in creating awareness on Covid-19 with the final objective of destigmatizing the patients through the analysis of sentiments and emotions on Covid-19  and finally use the same result for containing the spread of the pandemic

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

scholarly journals Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

2021 ◽  
Vol 8 (2) ◽  
pp. 311
Author(s):  
Mohammad Farid Naufal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors

<p class="Abstrak">Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. <em>Computer vision</em> adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan <em>self autonomous machine</em> agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara <em>real time</em>. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari <em>Machine Learning</em> sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Prediction of Liver Diseases by Using Few Machine Learning Based Approaches

2020 ◽  
pp. 85-90
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Liver Diseases ◽  
Model Building ◽  
Medical Science ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors ◽  
Learning Techniques

Advancement in medical science has always been one of the most vital aspects of the human race. With the progress in technology, the use of modern techniques and equipment is always imposed on treatment purposes. Nowadays, machine learning techniques have widely been used in medical science for assuring accuracy. In this work, we have constructed computational model building techniques for liver disease prediction accurately. We used some efficient classification algorithms: Random Forest, Perceptron, Decision Tree, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) for predicting liver diseases. Our works provide the implementation of hybrid model construction and comparative analysis for improving prediction performance. At first, classification algorithms are applied to the original liver patient datasets collected from the UCI repository. Then we analyzed features and tweaked to improve the performance of our predictor and made a comparative analysis among the classifiers. We examined that, KNN algorithm outperformed all other techniques with feature selection.

scholarly journals Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

2019 ◽  
Vol 11 (11) ◽  
pp. 1279 ◽  
Author(s):  
Pramaditya Wicaksono ◽  
Prama Ardha Aryaguna ◽  
Wahyu Lazuardi
Keyword(s):  
Machine Learning ◽  
Classification Scheme ◽  
Learning Algorithm ◽  
Classification Tree ◽  
Habitat Mapping ◽  
Support Vector ◽  
Benthic Habitat ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Mapping Model

This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

scholarly journals Comparative Analysis of Machine Learning Algorithms with and without Feature Extraction

2020 ◽  
Vol 6 (12) ◽  
pp. 235-239
Author(s):  
Vatsal Gupta and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Image Recognition ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Security Services ◽  
Computational Resources

Image recognition is one of the core disciplines in Computer Vision. It is one of the most widely researched topics of the last few decades. Many advances in image recognition in the past decade, has made it one of the most efficient and powerful disciplines of all, having its applications in every sector including Finance, Healthcare, Security services, Agriculture and many more. Feature extraction is an integral part of image recognition. It helps in training the model more efficiently and with a higher accuracy, by getting rid of any unwanted or unnecessary features, thus reducing the dimensionality of the input image. This also helps in reducing the computational resources required by the algorithm to train, thus making it affordable for people with low end setups. Here we compare the accuracies of different machine learning classification algorithms, and their training times, with and without using feature Extraction. For the purpose of extracting features, a convolutional neural network was used. The model was trained and tested on the data of 12 classes containing a total of 2,175 images. For comparisons, we chose the Logistic regression, K-Nearest Neighbors Classifier, Random forest Classifier, and Support Vector Machine Classifier.

scholarly journals 5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

2018 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Lubna Farhi ◽  
Razia Zia ◽  
Zain Anwar Ali
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Mr Images ◽  
Data Set ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Brain Mr Images ◽  
Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

scholarly journals An Ontology Driven System to Predict Diabetes with Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4005-4011
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Diagnostic Center ◽  
Mental Trauma

Diabetes Mellitus is considered one of the chronic diseases of humankind which causes an increase in blood sugar. Many complications are reported if DM remains untreated and unidentified. Identification of this disease requires a lot of physical and mental trauma and effort which involves visiting a doctor, blood and urine test at the diagnostic center which consumes more time. Difficulties can be over crossed using the trending technology of Machine learning. The idea of the model is to prognosticate the occurrence of a diabetic with high accuracy. Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage.

An analysis of PCOS disease prediction model using machine learning classification algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Shivani Aggarwal ◽  
Kavita Pandey
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Metabolic Abnormalities ◽  
Related Disorder ◽  
Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

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