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 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 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 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.

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2296-2300 ◽  
Author(s):  
Fei Shuai ◽  
Jun Quan Li
Keyword(s):  
Computational Complexity ◽  
Information Security ◽  
Decision Tree ◽  
Recognition Algorithm ◽  
Space Complexity ◽  
Complex Relationship ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

In current, there are complex relationship between the assets of information security product. According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C4.5 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm. Finally, we demonstrate that our algorithm is more precise than C4.5 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm.Keywordsdecision tree, information security product, asset recognition, C4.5

scholarly journals C4.5 DECISION TREE IMPLEMENTATION IN SISTEM INFORMASI ZAKAT (SIZAKAT) TO AUTOMATICALLY DETERMINING THE AMOUNT OF ZAKAT RECEIVED BY MUSTAHIK

2014 ◽  
Vol 10 (1) ◽  
pp. 28 ◽  
Author(s):  
David Bayu Ananda ◽  
Ari Wibisono
Keyword(s):  
Information Systems ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Accuracy Rate ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Abstract In general, Zakat Information Systems is established to manage the zakat services, so that the data can be well documented. This study proposes the existence of a feature that will determine the amount of zakat received by Mustahik automatically using C4.5 Decision Tree algorithm. This feature is expected to make the process of determining the amount of zakat be done easy and optimal. The data used in this study are the data taken from Masjid An-Nur, Pancoran, South Jakarta. The experiment results show that the proposed feature produces an accuracy rate over 85%.

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