scholarly journals Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
D. Mudali ◽  
L. K. Teune ◽  
R. J. Renken ◽  
K. L. Leenders ◽  
J. B. T. M. Roerdink
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Fdg Pet ◽  
Imaging Techniques ◽  
Principal Component ◽  
Training Data ◽  
Brain Diseases ◽  
Parkinsonian Syndromes ◽  
Decision Tree Method ◽  
Tree Method

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

scholarly journals Decision trees in environmental justice research — a case study on the floods of 2001 and 2010 in Hungary

2019 ◽  
Vol 11 (1) ◽  
pp. 1025-1034 ◽  
Author(s):  
Gyula Nagy ◽  
György Vida ◽  
Lajos Boros ◽  
Danijela Ćirić
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Environmental Justice ◽  
Social Groups ◽  
Principal Component ◽  
Decision Making Processes ◽  
Main Factors ◽  
Decision Tree Method ◽  
Tree Method

Abstract Environmental justice is a normative framework for the analysis of environmental impacts on the wellbeing of individuals and social groups. According to the framework, the deprived social groups and ethnic minorities are often more exposed to environmental risks and hazards due to their disadvantaged situation, and due to the lack of representation and political power. To manage the impacts of injustices and to include the citizen in the decision-making processes, proper information is needed on local attitudes and decision-making processes. Therefore, this study sought to (i) identify the main factors shaping the attitudes towards environmental injustices and (ii) to analyse the attitudes and perception of the various social groups and (iii) to identify the main factors which are shaping the attitudes and actions of those who were affected by the floods of 2001 and 2010 through the use of decision tree method. The data for the predictive model was acquired from a questionnaire survey conducted in two disadvantaged and flood-hit Hungarian regions. Based on the survey data, a principal component analysis (PCA) was conducted, which resulted in three principal components; fear, social change, and change in the built environment. The study focused only on the elements of the “fear principal component”, due to the decision tree tool homogenous groups identified in relation to this component. Our analysis showed that ethnicity has a determinative role in the emergence and the level of fear from floods; the Roma respondents expressed a significantly higher level of fear than others.

scholarly journals Common Metamodel of Questionnaire Model and Decision Tree Model

2018 ◽  
Vol 10 (3) ◽  
pp. 106
Author(s):  
Mirza Suljic ◽  
Edin Osmanbegovic ◽  
Željko Dobrović
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Decision Tree Model ◽  
Tree Model ◽  
Meta Model ◽  
The Subject ◽  
Decision Tree Method ◽  
Questionnaire Method ◽  
Tree Method

The subject of this paper is metamodeling and its application in the field of scientific research. The main goal is to explore the possibilities of integration of two methods: questionnaires and decision trees. The questionnaire method was established as one of the methods for data collecting, while the decision tree method represents an alternative way of presenting and analyzing decision making situations. These two methods are not completely independent, but on the contrary, there is a strong natural bond between them. Therefore, the result reveals a common meta-model that over common concepts and with the use of metamodeling connects the methods: questionnaires and decision trees. The obtained results can be used to create a CASE tool or create repository that can be suitable for exchange between different systems. The proposed meta-model is not necessarily the final product. It could be further developed by adding more entities that will keep some other data.

scholarly journals ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

2018 ◽  
Vol 2 (2) ◽  
pp. 178
Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Training Data ◽  
Prediction System ◽  
Tree Model ◽  
Average Accuracy ◽  
C4.5 Algorithm ◽  
Academic Grades ◽  
Decision Tree Method ◽  
Tree Method

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

scholarly journals Decision Tree Method Using for Fetal State Classification from Cardiotography Data

2020 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Md Zannatul Arif ◽  
Rahate Ahmed ◽  
Umma Habiba Sadia ◽  
Mst Shanta Islam Tultul ◽  
Rocky Chakma
Keyword(s):  
Heart Rate ◽  
Decision Tree ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Classification Tree ◽  
Training Data ◽  
Single Node ◽  
Data Set ◽  
Decision Tree Method ◽  
Tree Method

The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method. Cardiotocography is one of the important tools for monitoring heart rate, and this technique is widely used worldwide. Cardiotocography is applied for diagnosing pregnancy and checking fetal heart rate state condition until before delivery. This classification is necessary to predict fetal heart rate situation which is belonging. In this paper, we are using three input attributes of training data set quoted by LB, AC, and FM to categorize as normal, suspect or pathological where NSPF variable is used as a response variable. After drawing necessary analysis into three variables we get the 19 nodes of classification tree and also we have measured every single node according to statistic, criterion, weights, and values. The Cardiotocography Dataset applied in this study is received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the highest accuracy is 98.7%. Overall, the experimental results proved the viability of Classification and Regression Trees and its potential for further predictions.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited. 

scholarly journals Using Decision Trees for Identification of Most Relevant Indicators for Effective ICT Utilization

2013 ◽  
Vol 13 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Silvana Tomic Rotim ◽  
Jasminka Dobsa ◽  
Zdravko Krakar
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
World Economic Forum ◽  
Effective Utilization ◽  
The World ◽  
World Economic ◽  
Main Factors ◽  
Decision Tree Method ◽  
Tree Method

Abstract This paper offers a brief overview of the research of ICT utilization and benefits of its usage. The results of several important studies conducted worldwide are presented. One of them is a study by the World Economic Forum that we use as the basis of our research. This study covers 134 countries, NRI (Network Readiness Index) is used as a parameter to distinguish the readiness of different countries to adopt ICT. NRI consists of 68 indicators that are organized into three groups. Each group describes one of the three main factors crucial for effective utilization of ICT: Environment, Readiness and Usage. The observed countries are divided into four groups (leaders, followers, league III and league IV) and classification by a decision tree is conducted. The decision tree method is applied to each of the three main factors and the results are presented by means of F1 measure.

scholarly journals Klasifikasi Penentuan Jenis Obat Menggunakan Algoritma Decision Tree

2021 ◽  
Vol 7 (3) ◽  
pp. 53-60
Author(s):  
Rika Nursyahfitri ◽  
Alfanda Novebrian Maharadja ◽  
Riva Arsyad Farissa ◽  
Yuyun Umaidah
Keyword(s):  
Decision Tree ◽  
Medical Records ◽  
Training Data ◽  
Accuracy Rate ◽  
Data Set ◽  
Drug Determination ◽  
Decision Tree Method ◽  
The Relationship ◽  
Tree Method

Classification is a technique that can be used for prediction, where the predicted value is a label. The classification of drug determination aims to predict the type of drug that is accurate for patients with the dataset that has been obtained. The data used in this study are data from the patient's medical records based on the symptoms of the disease but the type of medicine is not yet known. The data set used comes from kaggle.com which is then presented in the form of a decision tree with a mathematical model. To complete this research, a classification method is used in data mining, namely the decision tree. The decision tree method is used to find the relationship between a number of candidate variables, so that it becomes a classification target variable by dividing the data into 70% data testing and 30% training data. The results obtained from this study are in the form of rules and an accuracy rate of 96.36% as well as the recall and precision values ​​of each type of drug using a multiclass configuration matrix.

scholarly journals An Approach for Predicting the Shape and Size of a Buried Basic Object on Surface Ground Penetrating Radar System

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Nana Rachmana Syambas
Keyword(s):  
Decision Tree ◽  
Ground Penetrating Radar ◽  
Rectangular Cross Section ◽  
Training Model ◽  
Training Data ◽  
Buried Objects ◽  
Basic Object ◽  
Decision Tree Method ◽  
Ground Penetrating ◽  
Tree Method

Surface ground-penetrating radar (GPR) is one of the radar technology that is widely used in many applications. It is nondestructive remote sensing method to detect underground buried objects. However, the output target is only hyperbolic representation. This research develops a system to identify a buried object on surface GPR based on decision tree method. GPR data of many basic objects (with circular, triangular, and rectangular cross-section) are classified and extracted to generate data training model as a unique template for each type of basic object. The pattern of object under test will be known by comparing its data with the training data using a decision tree method. A simple powerful algorithm to extract feature parameters of object which is based on linear extrapolation is proposed. The result showed that tested buried basic objects can be correctly predicted and the developed system works properly.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

A C4.5 Fuzzy Decision Tree Method for Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Rafael R. C. Silva ◽  
Walmir Matos Caminhas ◽  
Petronio Candido de Lima e Silva ◽  
Frederico Gadelha Guimaraes
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Decision Tree Method ◽  
Tree Method
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