Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance

2019 ◽  
Vol 72 (6) ◽  
pp. 431-437 ◽  
Author(s):  
Laura Bigorra ◽  
Iciar Larriba ◽  
Ricardo Gutiérrez-Gallego
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Blood Cell ◽  
White Blood Cell ◽  
Population Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Algorithm ◽  
Erythrocyte Lysis ◽  
Wbc Count

AimsRed blood cell (RBC) lysis resistance interferes with white blood cell (WBC) count and differential; still, its detection relies on the identification of an abnormal scattergram, and this is not clearly adverted by specific flags in the Beckman-Coulter DXH-800. The aims were to analyse precisely the effect of RBC lysis resistance interference in WBC counts, differentials and cell population data (CPD) and then to design, develop and implement a novel diagnostic machine learning (ML) model to optimise the detection of samples presenting this phenomenon.MethodsWBC counts, differentials and CPD from 232 patients (anaemia or liver disease) were compared with 100 healthy controls (HC) using analysis of variance. The data were analysed after a corrective action, and the analyser differentials were also compared with the digital leucocyte differentials. The ML support vector machine (SVM) algorithm was trained with 70% of the samples (n=233) and the 30% remaining (n=99) were employed exclusively during the validation phase.ResultsWe identified that impedance WBC was not affected by the RBC lysis resistance interference while the DXH-800 differentials overestimated lymphoid subpopulations (17.6%), sometimes even yielding spurious lymphocytosis, and the latter were corrected when sample dilution was performed. The ML-SVM algorithm allowed the classification of the pathological groups when compared with HC with validation accuracies corresponding to 97.98%, 100% and 88.78% for the global, anaemia and liver disease groups, respectively.ConclusionsThe proposed algorithm has an impressive discriminatory potential and its application would be a valuable support system to detect spurious results due to RBC lysis resistance.

scholarly journals Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

2020 ◽  
Vol 10 (15) ◽  
pp. 5047 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Danesh Zandi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Algorithm ◽  
Alternating Decision Tree ◽  
Bayesian Logistic Regression

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

scholarly journals Glass Classification based on Machine Learning Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 139-142
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Amorphous Solid ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Svm Algorithm ◽  
Artificial Neural Network Ann ◽  
Logistic Regression Algorithm

Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Logistic Regression algorithm. By comparing all the algorithm Random Forest did the best in glass classification.

scholarly journals Analyzing Graduation Project Ideas by using Machine Learning

2021 ◽  
Vol 15 (23) ◽  
pp. 136-147
Author(s):  
Hajar A. Alharbi ◽  
Hessa I. Alshaya ◽  
Meshaiel M. Alsheail ◽  
Mukhlisah H. Koujan
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Scientific Journals ◽  
Svm Algorithm ◽  
Graduation Project ◽  
Small Dataset

The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.

scholarly journals Statistical Machine Learning Approaches to Liver Disease Prediction

Livers ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 294-312
Author(s):  
Fahad Mostafa ◽  
Easin Hasan ◽  
Morgan Williamson ◽  
Hafiz Khan
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Missing Data ◽  
Health Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Statistical Machine Learning ◽  
Medical Diagnoses

Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.

scholarly journals Analytics of stock market prices based on machine learning algorithms

2019 ◽  
Vol 16 (2) ◽  
pp. 1050
Author(s):  
Puteri Hasya Damia Abd Samad ◽  
Sofianita Mutalib ◽  
Shuzlina Abdul-Rahman
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Types ◽  
Market Prices ◽  
Financial News ◽  
Svm Algorithm

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.

scholarly journals Machine Learning in the Climatic Database: Support Vector Machine’s Algorithm to Map Environmental Events in Months of the Year

2018 ◽  
Author(s):  
Gilvandro De Medeiros ◽  
Orivaldo De Santana Junior ◽  
John Luiz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Intelligence ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Support Vector ◽  
Svm Algorithm ◽  
Climatic Event ◽  
Environmental Events ◽  
The City

With the dissemination of Artificial Intelligence (AI), it becomes common the application of machine learning algorithms (ML) to model and solve problems. In this context, we intend to validate the performance of the ML Vector Support Machine (SVM) algorithm using a public climatic database for the city of Natal. The methodology for this consisted of using the data of said base to train and test the algorithm, placing the information referring to the month of the year in function of the other variables of a given climatic event. Once validated, it is considered promising to deepen the study and application of computational intelligence for meteorological and environmental purposes.

scholarly journals Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm

2021 ◽  
Vol 309 ◽  
pp. 01046
Author(s):  
Sarangam Kodati ◽  
Kumbala Pradeep Reddy ◽  
Sreenivas Mekala ◽  
PL Srinivasa Murthy ◽  
P Chandra Sekhar Reddy
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Personal Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Reduction Techniques ◽  
Svm Algorithm ◽  
The Social ◽  
False News

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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