A Machine Learning Approach to Detect Student Dropout at University

2021 ◽  
Vol 10 (6) ◽  
pp. 3101-3107
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Dropout Rate ◽  
Random Forest Classifier ◽  
Drop Out ◽  
Dropout Rates ◽  
Learning Technology ◽  
Student Dropout ◽  
High Dropout Rate ◽  
Academic Plan

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

scholarly journals Dropout in schema therapy for personality disorders

2018 ◽  
Vol 21 (2) ◽  
Author(s):  
İsmail Volkan Gülüm
Keyword(s):  
Personality Disorders ◽  
Dropout Rate ◽  
Drop Out ◽  
Dropout Rates ◽  
Schema Therapy ◽  
Weighted Mean ◽  
Group Settings ◽  
Research Findings ◽  
Meta Analyses ◽  
Outpatient Settings

Schema therapy (ST) is a relatively new, but promising, psychotherapy approach. Able to be implemented in both individual and group settings, research findings suggest that ST is a highly effective treatment for personality disorders. As in other treatments for personality disorders, some patients decide to drop out from treatment, feeling they did not benefit. To date, there has been no study in the literature that investigates the dropout rates across ST studies specifically. Consequently, this study systematically researched eight different ST studies in which dropout rates were reported. Together, these studies featured both individual and group therapy settings, inpatient and outpatient settings, and different personality disorder diagnoses. The weighted mean dropout rate was 23.3%, 95% CI (14.8-31.7%) across these studies. Although this finding is very similar to those meta-analyses that obtained their dropout rates from different orientations and diagnoses, namely psychotherapy in general, ST’s dropout rates might be significantly lower than studies that included personality disorders in particular.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

2017 ◽  
Vol 25 (3) ◽  
pp. 811-827 ◽  
Author(s):  
Dimitris Spathis ◽  
Panayiotis Vlamos
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Random Forest ◽  
Pulmonary Disease ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Forced Expiratory Volume ◽  
Random Forest Classifier ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

scholarly journals Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

2016 ◽  
Vol 23 (2) ◽  
pp. 124 ◽  
Author(s):  
Douglas Detoni ◽  
Cristian Cechinel ◽  
Ricardo Araujo Matsumura ◽  
Daniela Francisco Brauner
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
At Risk Students ◽  
Drop Out ◽  
Support Vector ◽  
Learning Models ◽  
Data Set ◽  
Student Dropout ◽  
Vector Machines ◽  
Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

scholarly journals Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

2020 ◽  
Vol 17 (24) ◽  
pp. 9378
Author(s):  
Amy Marie Campbell ◽  
Marie-Fanny Racault ◽  
Stephen Goult ◽  
Angus Laurenson
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Land Surface ◽  
Environmental Changes ◽  
Random Forest Classifier ◽  
Sea Surface Salinity ◽  
Learning Approach ◽  
Climate Variables ◽  
Surface Salinity ◽  
Machine Learning Approach

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

Enhancing alpine glacial lakes detection and mapping using multi-source data and machine learning techniques

2020 ◽  
Author(s):  
Sonam Wangchuk ◽  
Tobias Bolch
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Satellite Images ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Glacial Lake ◽  
Glacial Lakes ◽  
Alpine Regions ◽  
Learning Techniques ◽  
Source Data

<p>An accurate detection and mapping of glacial lakes in the Alpine regions such as the Himalayas, the Alps and the Andes are challenged by many factors. These factors include 1) a small size of glacial lakes, 2) cloud cover in optical satellite images, 3) cast shadows from mountains and clouds, 4) seasonal snow in satellite images, 5) varying degree of turbidity amongst glacial lakes, and 6) frozen glacial lake surface. In our study, we propose a fully automated approach, that overcomes most of the above mentioned challenges, to detect and map glacial lakes accurately using multi-source data and machine learning techniques such as the random forest classifier algorithm. The multi-source data are from the Sentinel-1 Synthetic Aperture Radar data (radar backscatter), the Sentinel-2 multispectral instrument data (NDWI), and the SRTM digital elevation model (slope). We use these data as inputs for the rule-based segmentation of potential glacial lakes, where decision rules are implemented from the expert system. The potential glacial lake polygons are then classified either as glacial lakes or non-glacial lakes by the trained and tested random forest classifier algorithm. The performance of the method was assessed in eight test sites located across the Alpine regions (e.g. the Boshula mountain range and Koshi basin in the Himalayas, the Tajiks Pamirs, the Swiss Alps and the Peruvian Andes) of the word. We show that the proposed method performs efficiently irrespective of geographic, geologic, climatic, and glacial lake conditions.</p>

scholarly journals A Comparative Study using Feature Selection to Predict the Behaviour of Bank Customers

2020 ◽  
Vol 184 ◽  
pp. 01011
Author(s):  
Sreethi Musunuru ◽  
Mahaalakshmi Mukkamala ◽  
Latha Kunaparaju ◽  
N V Ganapathi Raju
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Customer Behavior ◽  
Machine Learning Algorithms ◽  
The Status ◽  
Personal Level ◽  
Near Future ◽  
Structure Communication

Though banks hold an abundance of data on their customers in general, it is not unusual for them to track the actions of the creditors regularly to improve the services they offer to them and understand why a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial to the banks as they can reach out to their customers on a personal level and develop a business model that will improve the pricing structure, communication, advertising, and benefits for their customers and themselves. Features like the amount a customer credits every month, his salary per annum, the gender of the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be continuing with them and who will be leaving them in the near future. Our study determines to remove the features that are independent but are not influential to determine the status of the customers in the future without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the results.

scholarly journals An exploration into students' perceptions regarding dropout within the chiropractic programme at a University of Technology

2018 ◽  
Author(s):  
◽  
Nqubeko Lizwilenkosi Buthelezi
Keyword(s):  
South Africa ◽  
Data Analysis ◽  
Student Teaching ◽  
Policy Development ◽  
Dropout Rate ◽  
The Body ◽  
Drop Out ◽  
Student Dropout ◽  
Contextual Design ◽  
University Of Technology

Introduction: Chiropractic is a health profession specialising in the diagnosis, treatment and prevention of disorders affecting the bones, joints, muscles and nerves in the body. It is a type of alternative or complimentary medicine concerned with the relationship between the body's structure and its functioning. The Durban University of Technology (DUT) and University of Johannesburg are the two internationally accredited academic institutions in South Africa to offer the chiropractic programme. The Chiropractic Department at the DUT is one of 13 departments within the Faculty of Health Sciences. A student who successfully completes the chiropractic-training programme becomes registered as doctor of chiropractic by the Allied Health Professions Council of South Africa under Act 63 of 1982 (as amended). However, a number of students drop out from the chiropractic programme before completion. Some of these students transfer to other programmes; others deregister and leave the university, while others are excluded because of the progression rule or because of having exceeded the maximum duration of the programme. Aim of the study: The aim of the study was to explore and describe the perceptions of the students regarding dropping out from the chiropractic programme at the DUT. The study aimed to answer three research questions, which were: 1) what are the perceptions of students regarding dropout from the chiropractic programme at the DUT? 2) what are the determinants of student dropout from the chiropractic programme at the DUT? and 3) how can the dropout rate in the chiropractic programme at the DUT be minimised? Methodology: A qualitative, explorative, descriptive and contextual design was employed. The DUT was used as a data collection site. Data was collected between May and June 2018 using one-on-one semi structured interviews with 12 former students who were previously registered for the chiropractic programme and dropped out before completion. Tesch’s eight steps of data analysis guided thematic data analysis. Findings: The students’ perceptions regarding dropout from the chiropractic programme were grouped into five major themes and several subthemes. The major themes included financial constraints, post course employment, personal, course related and socio- cultural factors. All these themes were, according to the participants, determinants of student dropout from the chiropractic programme. Recommendation from the study findings focused on how the dropout rate in the chiropractic programme could be minimised. Conclusion: The study discovered that, according to the students’ perceptions, there are several determinants of the high dropout rate from the chiropractic programme. Some of these are intrinsic chiropractic programme factors such as course structure, workload and assessment strategy. However, other determinants are outside the programme and generic to all university disciplines/programmes. Nevertheless, it is still critical that attention be given to all determining factors to facilitate retention of students into the chiropractic programme. Recommendations: The following recommendations with special reference to policy development and implementation, institutional management and practice, chiropractic education and further research, are presented. The national and institutional policies regarding application and administration of financial aid should be reviewed and guidelines for application and appeals procedures should be made known to students. Student teaching and assessment strategies should be reviewed periodically and input from students be invited. The Chiropractic Department should ensure that information about the programme and qualification is made available to the public. The chiropractic curriculum should include entrepreneurship to provide information and guidance on how to set up own private practice. The chiropractic programme should institute measures of decolonising the programme in order to address challenges of racial discrimination. A broader research study on reasons for student dropout is recommended.

scholarly journals Stock Picker using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3912-3914
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Market ◽  
Stock Price ◽  
Historical Data ◽  
Great Accuracy ◽  
Current Data ◽  
Random Forest Classifier ◽  
Market Prices ◽  
Market Institution

The main objective of this paper is to build a model to predict the value of stock market prices from the previous year's data. This project starts with collecting the stock price data and pre-processing the data. 12 years dataset is used to train the model by the Random Forest classifier algorithm. Backtesting is the most important part of the quantitative strategy by which the accuracy of the model is obtained. Then the current data is collected from yahoo finance and the data is fed to the model. Then the model will predict the stock that is going to perform well based on its learning from the historical data. This model predicted the stocks with great accuracy and it can be used in the stock market institution for finding the good stock in that index.

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