scholarly journals Internet of things based Early Detection of Diabetes using Machine Learning Algorithms: Dpa

2019 ◽  
Vol 8 (10) ◽  
pp. 1443-1447
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Early Detection ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Early Prediction ◽  
Decision Tree Algorithm ◽  
Diabetes Prediction ◽  
Prediction Of Diabetes

This paper introduces a new decision tree algorithm Diabetes Prediction Algorithm (DPA), for the early prediction of diabetes based on the datasets. The datasets are collected by using Internet of Things (IOT) Diabetes Sensors, comprises of 15000 records, out of which 11250 records are used for training purpose and 3750 are used for testing purpose. The proposed algorithm DPA yielded an accuracy of 90.02 %, specificity of 92.60 %, and precision of 89.17% and error rate of 9.98%. further, the proposed algorithm is compared with existing approaches. Currently there are numerous algorithms available which are not complete accurate and DPA helps.

scholarly journals Selección de tutores académicos en la educación superior usando árboles de decisión

2018 ◽  
Vol 29 (1) ◽  
pp. 108
Author(s):  
Argelia B. Urbina Nájera ◽  
Jorge De la Calleja
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Community ◽  
Machine Learning Algorithms ◽  
Voluntary Participation ◽  
Educación Superior ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Educacion Superior ◽  
Real World Problems

RESUMEN  En este documento se presenta un método para mejorar el proceso de tutoría académica en la educación superior. El método incluye a identificación de las habilidades principales de los tutores de forma automática utilizando el algoritmo árboles de decisión, uno de los algoritmos más utilizados en la comunidad de aprendizaje automático para resolver problemas del mundo real con gran precisión. En el estudio, el algoritmo arboles de decisión fue capaz de identificar las habilidades y afinidades entre estudiantes y tutores. Los experimentos se llevaron a cabo utilizando un conjunto de datos de 277 estudiantes y 19 tutores, mismos que fueron seleccionados por muestreo aleatorio simple y participación voluntaria en el caso de los tutores. Los resultados preliminares muestran que los atributos más importantes para los tutores son la comunicación, la autodirección y las habilidades digitales. Al mismo tiempo, se presenta un proceso de tutoría en el que la asignación del tutor se basa en estos atributos, asumiendo que puede ayudar a fortalecer las habilidades de los estudiantes que demanda la sociedad actual. De la misma forma, el árbol de decisión obtenido se puede utilizar para agrupar a tutores y estudiantes basados en sus habilidades y afinidades personales utilizando otros algoritmos de aprendizaje automático. La aplicación del proceso de tutoría sugerido podría dar la pauta para ver el proceso de tutoría de manera individual sin vincularla a procesos de desempeño académico o deserción escolar.ABSTRACTIn this paper, we present a method for the tutoring process in order to improve academic tutoring in higher education. The method includes identifying the main skills of tutors in an automated manner using decision trees, one of the most used algorithms in the machine learning community for solving several real-world problems with high accuracy. In our study, the decision tree algorithm was able to identify those skills and personal affinities between students and tutors. Experiments were carried out using a data set of 277 students and 19 tutors, which were selected by random sampling and voluntary participation, respectively. Preliminary results show that the most important attributes for tutors are communication, self-direction and digital skills. At the same time, we introduce a tutoring process where the tutor assignment is based on these attributes, assuming that it can help to strengthen the student's skills demanded by today's society. In the same way, the decision tree obtained can be used to create cluster of tutors and clusters of students based on their personal abilities and affinities using other machine learning algorithms. The application of the suggested tutoring process could set the tone to see the tutoring process individually without linking it to processes of academic performance or school dropout.

scholarly journals Research Ancient Artifact Identification Methods under Intelligent Perception and Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qiang Zhao
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
South China ◽  
Archaeological Site ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Chinese People ◽  
Identification Methods ◽  
Challenging Tasks

Over the last two decades, the identification of ancient artifacts has been regarded as one of the most challenging tasks for archaeologists. Chinese people consider these artifacts as symbols of their cultural heritage. The development of technology has helped in the identification of ancient artifacts to a greater extent. The study preferred machine-learning algorithms to identify the ancient artifacts found throughout China. The major cities of China were selected for the study and classified the cities based on different features like temple, modern city, harbour, battle, and South China. The study used a decision tree algorithm for recognition and gradient boosting for perception aspects. According to the findings of the study, the algorithms produced 98% accuracy and prediction in detecting ancient artifacts in China. The proposed models provide a good indicator for detecting archaeological site locations.

scholarly journals Prediction of COVID-19 Diagnosis Based on OpenEHR Artefacts

2021 ◽  
Author(s):  
Daniela Oliveira ◽  
Diana Ferreira ◽  
Nuno Abreu ◽  
Pedro Leuschner ◽  
António Abelha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Decision Tree ◽  
Home Care ◽  
Predictive Model ◽  
Machine Learning Algorithms ◽  
Health Conditions ◽  
Evolutionary Status ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Abstract The complexity and momentum of monitoring COVID-19 patients calls for the usage of agile and scalable data structure methodologies. A system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 infected patients was developed based on the openEHR architecture. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different machine learning algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, and a specificity of 99.92%, using the Decision Tree algorithm and the Split Validation method.

scholarly journals Early prediction of diabetes using Feature Transformation and hybrid Random Forest Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 787-791
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Experimental Result ◽  
Early Prediction ◽  
Learning Approaches ◽  
Feature Transformation ◽  
Common Chronic Disease ◽  
Diabetes Prediction ◽  
Diagnosis Accuracy ◽  
Prediction Of Diabetes

Diabetes is the most common chronic disease among the world. Early prediction of these will assist the physicians to provide the improved treatment. Machine learning approaches are widely used for predicting the disease at the earlier stage. However the selecting the significant features and the suitable classifier are still reduces the diagnosis accuracy. In this paper the PCA based feature transformation and the hybrid random forest classifier is utilized for diabetes prediction. PCA attempt to identify the best subset of transformed components that greatly improves the classification result. The system is compared with priori machine learning approaches to evaluate the efficiency of this work. The experimental result shows that the present study enhances the prediction accuracy.

scholarly journals Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Maad M. Mijwil ◽  
Rana A. Abttan
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Continuous Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Confidence Factor ◽  
C4.5 Decision Tree

A decision tree (DTs) is one of the most popular machine learning algorithms that divide data repeatedly to form groups or classes. It is a supervised learning algorithm that can be used on discrete or continuous data for classification or regression. The most traditional classifier in this algorithm is the C4.5 decision tree, which is the point of this research. This classifier has the advantage of building a vast data set and does not stop until it reaches the desired goal. The problem with this classifier is that there are unnecessary nodes and branches leading to overfitting. This overfitting can negatively affect the classification process. In this context, the authors suggest utilizing a genetic algorithm to prune the effect of overfitting. This dataset study consists of four datasets: IRIS, Car Evaluation, GLASS, and WINE collected from UC Irvine (UCI) machine learning repository. The experimental results have confirmed the effectiveness of the genetic algorithm in pruning the effect of overfitting on the four datasets and optimizing confidence factor (CF) of the C4.5 decision tree. The proposed method has reached about 92% accuracy in this work.

scholarly journals An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1115
Author(s):  
Jaehak Yu ◽  
Sejin Park ◽  
Hansung Lee ◽  
Cheol-Sig Pyo ◽  
Yang Sun Lee
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Rapid Change ◽  
Machine Learning Algorithms ◽  
Semantic Interpretation ◽  
Stroke Severity ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Depth Analysis ◽  
C4.5 Decision Tree

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

scholarly journals Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease

2021 ◽  
Vol 36 (1) ◽  
pp. 713-720
Author(s):  
S.K.L. Sameer ◽  
P. Sriramya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

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