scholarly journals Visual Analytics Design for Students Assessment Representation based on Supervised Learning Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 43-49
Author(s):  
Adlina Abdul Samad ◽  
Marina Md Arshad ◽  
Maheyzah Md Siraj ◽  
Nur Aishah Shamsudin
Keyword(s):  
Decision Tree ◽  
Visual Analytics ◽  
Student Assessment ◽  
Analytical Models ◽  
Support Vector ◽  
Assessment Data ◽  
Tree Model ◽  
Supervised Learning Algorithms ◽  
Tree Models ◽  
Intended Learning Outcomes

Visual Analytics is very effective in many applications especially in education field and improved the decision making on enhancing the student assessment. Student assessment has become very important and is identified as a systematic process that measures and collects data such as marks and scores in a manner that enables the educator to analyze the achievement of the intended learning outcomes. The objective of this study is to investigate the suitable visual analytics design to represent the student assessment data with the suitable interaction techniques of the visual analytics approach. sheet. There are six types of analytical models, such as the Generalized Linear Model, Deep Learning, Decision Tree Model, Random Forest Model, Gradient Boosted Model, and Support Vector Machine were used to conduct this research. Our experimental results show that the Decision Tree Models were the fastest way to optimize the result. The Gradient Boosted Model was the best performance to optimize the result.

scholarly journals A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Jozef Zurada ◽  
Peng C. Lam
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Preliminary Investigation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Consumer Loans ◽  
Tree Models ◽  
Accuracy Rates

For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules.

Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment

2016 ◽  
Vol 24 (11) ◽  
pp. 1547-1556 ◽  
Author(s):  
Jesse C. Bledsoe ◽  
Cao Xiao ◽  
Art Chaovalitwongse ◽  
Sonya Mehta ◽  
Thomas J. Grabowski ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Sensitivity And Specificity ◽  
Neuropsychological Assessment ◽  
Feature Selection Method ◽  
Diagnostic Classification ◽  
Support Vector ◽  
Tree Model ◽  
Children With Adhd

Objective: Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD–Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires. Method: We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model. Results: The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity. Conclusion:This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.

scholarly journals Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Literature Review ◽  
Decision Tree ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Quantitative Studies ◽  
Safety Outcomes ◽  
Tree Models

BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

scholarly journals Analysis of Indian Food Based on Machine learning Classification Models

2021 ◽  
pp. 1-7
Author(s):  
Sasmita Kumari Nayak ◽  
Mamata Beura ◽  
Mohammed Siddique ◽  
Siba Prasad Mishra
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Human Life ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbor ◽  
Tree Model ◽  
Machine Learning Classification ◽  
Indian Food

For human life, Food is highly necessary and essential for human to live the life. The objective of the current study is to characterise, classify and compare the food consumption patterns of many Indian food diets such as non-vegetarian and vegetarian. Given data about different Indian dishes, we try to predict here the dish is vegetarian or not. To get the best predictive model, this study is conducted with the comparison of Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest algorithms. In this study, the concept and implementation of all these four models be made for prediction of Indian food. For training and testing the models, Indian food dataset is used that contains, in total 255 records to fit with all these four models. In short, the classification and prediction of Decision tree and KNN model provides less performance than the other models used here. However, the Random Forest model was generally more accurate than SVM, KNN and Decision Tree model, which have got from the simulation. 

scholarly journals Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

10.2196/18599 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18599 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Literature Review ◽  
Decision Tree ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Quantitative Studies ◽  
Safety Outcomes ◽  
Tree Models

Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

The Identification Method of Igneous Rock Lithology Based on Data Mining Technology

2012 ◽  
Vol 466-467 ◽  
pp. 65-69 ◽  
Author(s):  
Jun Zhang ◽  
Jun Li ◽  
Yao Hu ◽  
Jin Yu Zhou
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Igneous Rock ◽  
Decision Tree Model ◽  
Support Vector ◽  
Tree Model ◽  
Gas Field ◽  
Identification Method ◽  
Lithology Identification ◽  
Rock Lithology

Igenous rock is featured with complex and multivariant lithology, its logging response is multiplicity, therefore, it is difficult to identify igneous rock lithology with logging data. To Lay the foundation for fine logging evaluation of igneous reservoir in Songnan gas field, the identification method of igneous rock lithology is researched. Common crossplot method identify lithology with only two logging parameters, its precision is not high. To improve the accuracy of identification, the data mining software, named as weak, is used, three data mining methods, including Association Rule, Decision Tree, Support vector machine, are applied in lithology identification. The results show that these methods can improve the accuracy of lithology identification, in particular, Decision Tree model has the highest recognition accuracy, while it is relatively easy to understand, so it can be used as auxiliary tools for recognition of igneous rocks. Decision Tree model is used to process logging data of exploratory well, and its computation results are well consistent with the core thin section data.

scholarly journals Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

2021 ◽  
Vol 8 (1) ◽  
pp. 978
Author(s):  
Aziz Nurul Iman ◽  
Aji Gautama Putrada ◽  
Sidik Prabowo ◽  
Doan Perdana
Keyword(s):  
Body Temperature ◽  
Decision Tree ◽  
General Purpose ◽  
Test Results ◽  
Thermal Camera ◽  
Tree Model ◽  
Temperature Detection ◽  
Tree Models ◽  
Haar Cascade ◽  
Cascade Method

One way to prevent the spread of the COVID-19 virus is to check body temperature regularly. However, checking body temperature manually by directing the thermogun at someone's face is still often found. This study implements the use of the AMG8833 thermal camera to detect a person's body temperature without making any contact. The AMG8833 is a general-purpose temperature detection camera so to be used as a temperature meter, its accuracy needs to be improved by regression. The purpose of this research is to improve the performance of AMG833 as a thermal camera with AdaBoost regression. AdaBoost is a type of ensemble learning that uses several decision tree models. For face detection, the system uses the Haar Cascade method. The test results show that the decision tree model produces an R-Squared value of 0.93 and an RMSE of 0.21. Meanwhile, AdaBoost succeeded in improving the performance of the regression model with a higher R-Squared value and a lower RMSE value with values of 0.95 and 0.18, respectively.

scholarly journals "Predicting Absenteeism at Work Using Machine Learning Algorithms

2019 ◽  
Vol 7 (1) ◽  
pp. 1-12
Author(s):  
Samir Qaisar Ajmi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Tree Model ◽  
Learning Techniques ◽  
Business Market ◽  
Commercial Environment

"To work in the commercial environment, the company needs to be a major competitor in the business market, which depends mainly on the company's resources. One of the most important resources is the employees. Based on that, the absence of the employees from work leads to deterioration and reduce production in the institutions which leads to heavy losses. There are many reasons why employees are absent from work. Those may include health problems and social occasions. The purpose of this paper was to apply machine learning techniques to predict the absenteeism at work. There are four methods have been used in this research ( neural network(NN) technique ,decision tree (DT) technique, support vector machine (SVM) technique and logistic regression (LR) technique. . decision tree model has the highest accuracy equals to 83.33% with AUC 0.834 and the support vector machine has the lowest accuracy equals to 68.47 % with AUC 0.760."

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