scholarly journals Human Papillomavirus Targeted Immunotherapy Outcome Prediction Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3598-3611
Author(s):  
Vidya Moni
Keyword(s):  
Machine Learning ◽  
Human Papillomavirus ◽  
Outcome Prediction ◽  
Performance Comparison ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Nearest Neighbours ◽  
Vector Machines ◽  
Modern Machine

Warts caused by the Human Papillomavirus (HPV) is a highly contagious disease, and affects several million people across the globe every year, in the form of small lesions on the skin, commonly known as warts. Warts can be treated effectively with several methods, the most effective being Immunotherapy and Cryotherapy. Our research is focused on the performance comparison of modern Machine Learning classification techniques to predict the outcome (positive or negative) of Immunotherapy treatment given to a patient, by using patient data as input features to our classifiers. The precision, recall, f-measure and accuracy were used to compare the performance of the various classifiers considered in this study. We considered Logistic Regression, ZeroR, AdaBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gradient Boosting, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Decision Trees and Random Forests. The ZeroR classifier was used as a baseline to provide us with insights into the skewed nature of the data, so as to enable us to better understand the comparison in performance of the various classifiers.

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 Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Supervised machine learning based liver disease prediction approach with LASSO feature selection

2021 ◽  
Vol 10 (6) ◽  
pp. 3369-3376
Author(s):  
Saima Afrin ◽  
F. M. Javed Mehedi Shamrat ◽  
Tafsirul Islam Nibir ◽  
Mst. Fahmida Muntasim ◽  
Md. Shakil Moharram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Disease ◽  
Decision Tree ◽  
Medical Science ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Prediction Approach

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system. 

scholarly journals Applying Machine Learning for Healthcare: A Case Study on Cervical Pain Assessment with Motion Capture

2020 ◽  
Vol 10 (17) ◽  
pp. 5942 ◽  
Author(s):  
Juan de la Torre ◽  
Javier Marin ◽  
Sergio Ilarri ◽  
Jose J. Marin
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cervical Pain ◽  
K Nearest Neighbors ◽  
Network Algorithms ◽  
Vector Machines ◽  
Real Scenario

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.

scholarly journals Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

2021 ◽  
pp. 29-37
Author(s):  
Premkumar Borugadda ◽  
R. Lakshmi ◽  
Surla Govindu
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Performance Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cotton Leaf ◽  
Decision Tree Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier ◽  
Fully Connected

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.  

scholarly journals Students’ Class Performance Prediction Using Machine Learning Classifiers

2021 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Adeel Ahmed ◽  
Kamlesh Kumar ◽  
Mansoor A. Khuhro ◽  
Asif A. Wagan ◽  
Imtiaz A. Halepoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Measure ◽  
Faculty Members ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Vector Machines ◽  
Academic Records

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

P09.01 Radiomics-based machine learning approach in differentiation between vestibular schwannoma and meningioma in the cerebellopontine angle

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii26-ii26
Author(s):  
Y Zhang ◽  
C Chen ◽  
J Xu
Keyword(s):  
Machine Learning ◽  
Vestibular Schwannoma ◽  
Cerebellopontine Angle ◽  
Gradient Boosting ◽  
Surgical Approaches ◽  
Support Vector ◽  
Selection Methods ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Validation Set

Abstract BACKGROUND Vestibular schwannoma (VS) and meningioma are the most two common tumors in the cerebellopontine angle (CPA). Accurate preoperative differentiation of the two lesions is important due to their different surgical approaches and outcomes for the preservation of hearing and facial nerve function. Magnetic resonance (MR) scan is commonly performed to preoperatively evaluate CPA tumors and to differentiate VS from meningioma in clinical routine. However, in some cases, overlaps of conventional MR imaging patterns between the two lesions could make preoperative diagnosis challenging. The purpose of this study is to investigate the ability of radiomics, a novel method providing objective and quantitative information beyond visual assessment, in differentiation between VS and meningioma located at CPA using machine learning technology. MATERIAL AND METHODS This retrospective study enrolled eligible patients who were diagnosed with VS (N = 50) or meningioma (N = 41) in the CPA. A set of mineable three-dimensional radiomic parameters were extracted from preoperative contrast-enhanced T1-weighted images. Optimal features were selected first with three selection methods including distance correlation, least absolute shrinkage and selection operator (LASSO) and gradient boosting decision tree (GBDT). Then three machine learning classification algorithms, namely linear discriminant analysis (LDA), support vector machine (SVM) and random forest were employed to build discriminative models. Area under the curve (AUC), accuracy, sensitivity and specificity were calculated to assess the performance of each model. RESULTS Nine models were established with different combinations of selection methods and machine learning classifiers. Three classifiers with the suitable selection method all represented feasible ability in differentiation with AUC more than 0.86 in the validation set, and LDA-based models seemed to show better diagnostic performance than those based on the other two classifiers. The combination of LASSO and LDA classifier was found to show the highest AUC of 0.942 in the validation set. CONCLUSION Radiomics-based models via machine learning approaches could potentially be utilized to assist in preoperative differentiation between VS and meningioma in the CPA.

scholarly journals Student Intervention System using Machine Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2061-2065
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Student Records ◽  
Teaching Methodologies ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Learning Techniques ◽  
Vector Machines

Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.

scholarly journals A Machine Learning Practice on NAS Dataset: Influence of Socioeconomic Factors on Student Performance

2019 ◽  
Vol 8 (2) ◽  
pp. 3272-3275
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Nearest Neighbours ◽  
Proactive Measures ◽  
New Student

India’s population is enormous and diverse due to which its education system is very complex. Furthermore, due to several reasons that they have grown up in different environmental situations. Over the years, several changes have been suggested and implemented by various stakeholders to improve the quality of education in schools. This paper presents a novel method to predict the performance of a new student by the analysis of historical student data records, and furthermore, we explore the NAS dataset using cutting edge Machine Learning Algorithms to predict the grades of a new student and take proactive measures to help them succeed. Similarly, NAS Dataset can also be worthwhile to the employee dataset and can predict the performance of the employee. Some of the Supervised Machine Learning Algorithms for Classification which have been successfully applied to the NAS dataset. Support Vector Machines and K-Nearest Neighbours algorithms did not crop results in coherent time for the given dataset; Gradient Boosting Classifier outperformed than all other algorithms reliably

scholarly journals EEG-Based Classification of the Driver Alertness State

2020 ◽  
Vol 6 (3) ◽  
pp. 353-356
Author(s):  
Martin Golz ◽  
Sebastian Thomas ◽  
Adolf Schenka
Keyword(s):  
Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Weighting Matrix ◽  
Machine Learning Methods ◽  
Young Drivers ◽  
Eeg Data ◽  
Vector Machines ◽  
Generalized Matrix

AbstractGMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.

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