Marginalization of Women and Mass Markets

2020 ◽  
pp. 69-87
Author(s):  
José G. Hernández R. ◽  
María J. García G. ◽  
Gilberto J. Hernández G.
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Gender Equality ◽  
Support System ◽  
Free Market ◽  
Tree Structure ◽  
Mass Market

This chapter relates to gender equality in the free market, particularly the mass market. The objective of this work is to review if the woman is marginalized in the mass market through a decision tree structure. To achieve this overall objective, the integrated-adaptable methodology for the development of decision support system (MIASAD) is used. There are not enough elements to make a generalization, but, from the results obtained with the decision tree structure used in this investigation, it can be seen that there is no marginalization of women in the mass products market. This implies that no additional effort is necessary to minimize the marginalization of women in the mass market. As a consequence, efforts can focus on better segmentation of the female market. Thus, products that are more suitable for each of the sectors found can be generated.

Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study

2017 ◽  
Vol 16 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Kwang Hyeon Kim ◽  
Suk Lee ◽  
Jang Bo Shim ◽  
Kyung Hwan Chang ◽  
Yuanjie Cao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Predictive Modelling ◽  
Radiotherapy Planning ◽  
Support Vector ◽  
Toxicity Prediction ◽  
Planning Decision

AbstractPurposeThe aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system.Materials and methodsWe analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapyTM treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum.ResultsThe toxicity prediction algorithm analysis showed 91·0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible.ConclusionWe verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning.

scholarly journals RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN UNTUK PEMILIHAN LAPTOP DENGAN METODE DECISION TREE

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Dedi Anggaradana
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Rapid Development ◽  
High Mobility ◽  
Interactive System ◽  
Step Method ◽  
Step Step ◽  
The Right

ABSTRACT<br />A very rapid development of technology makes the laptop seems to have not a luxury<br />anymore. Those with high mobility and frequent travel, choose the laptop as something that<br />accompanies every job they wherever they went. But not a few consumers who are hesitant and<br />confused when trying to buy a new laptop since the laptop is not cheap stuff. Moreover currently<br />popping up various types of laptops with different specs and brand, any price varies. Not<br />infrequently the consumer to buy a laptop with good specs without thinking about its usefulness.<br />For that it is necessary a decision support system for laptop malakukan selection<br />process effectively and efficiently. Decision support system is an interactive system that supports<br />the decision in the decision making process through the alternative - the alternative obtained<br />from the data processing, information and design models.<br />Making an online decision support system using the decision tree method is able to<br />resolve the election issue this laptop, with a step - step method is simple, easily understood,<br />effective and efficient. With the web-based decision support system will be the technology that not<br />only provides convenience, but able to provide solutions to an individual in choosing the right<br />laptop and also fit your needs and budget.<br />Keywords : System, Decision Support, Decision Tree<br />ABSTRAK<br />Perkembangan teknologi yang sangat pesat membuat laptop sepertinya sudah bukan<br />menjadi barang mewah lagi. Mereka yang mobilitasnya tinggi dan sering bepergian, memilih<br />laptop sebagai benda yang menemani setiap pekerjaan mereka kemanapun mereka pergi.<br />Namun tak sedikit konsumen yang bimbang dan kebingungan saat ingin membeli laptop<br />baru mengingat laptop bukanlah barang murah. Terlebih lagi saat ini bermunculan berbagai<br />jenis laptop dengan beragam spesifikasi dan merk, pun harganya bervariasi. Tak jarang pula<br />konsumen membeli laptop dengan spesifikasi yang bagus tanpa memikirkan kegunaannya.<br />Untuk itu sangat diperlukan suatu sistem pendukung keputusan untuk malakukan<br />proses pemilihan laptop secara efektif dan efisien. Sistem pendukung keputusan merupakan suatu<br />sistem interaktif yang mendukung keputusan dalam proses pengambilan keputusan melalui<br />alternatif – alternatif yang diperoleh dari hasil pengolahan data, informasi dan rancangan model.<br />Pembuatan suatu sistem pendukung keputusan secara online dengan menggunakan<br />metode decision tree mampu menyelesaikan untuk persoalan pemilihan laptop ini, dengan<br />langkah – langkah metode ini yang sederhana, mudah dipahami, efektif dan efisien. Dengan<br />adanya sistem pendukung keputusan berbasis web ini akan menjadi teknologi yang tidak hanya<br />memberikan kemudahan, namun mampu memberikan solusi kepada seseorang dalam memilih<br />laptop yang tepat dan juga sesuai dengan kebutuhan dan anggaran.<br />Kata kunci : Sistem, Pendukung Keputusan, Decision Tree

scholarly journals An Intelligent Web-Based Decision Support System for Monitoring the Chronic Kidney Disease in Brazilian Communities

2020 ◽  
Author(s):  
Álvaro Sobrinho ◽  
Andressa C. M. da S. Queiroz ◽  
Gyovanne Bezerra Cavalcanti ◽  
Josaias de Moura Silva ◽  
Leandro Dias da Silva ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Decision Support ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Kappa Statistic ◽  
Web Based ◽  
Knowledge Based

Abstract Background: Chronic Kidney Disease (CKD) is a worldwide health problem, usually diagnosed in late stages of the disease, increasing public health costs and mortality rates. The late diagnosis is even more critical in developing countries due to the high levels of poverty, a large number of hard-to-reach locations, and sometimes lack/precarious primary care.Methods: We designed and evaluated an intelligent web-based Decision Support System (DSS) using the J48 decision tree machine learning algorithm, knowledge-based system concepts, the clinical document architecture, Cohen's kappa statistic, and interviews with an experienced nephrologist.Results: We provided a DSS methodology, that guided the development of the system to assist patients, primary care physicians, and the government in identifying and monitoring the CKD in Brazilian communities. The system provides remote monitoring features. A CKD dataset enabled the evaluation of the J48 decision tree algorithm, while Cohen's kappa statistic guided the evaluation of the knowledge-based system by interviews with an experienced nephrologist. Conclusion: The DSS facilitates the identification and monitoring of the CKD considering low-income populations in Brazil. In addition, the methodology and DSS can be re-used in other developing countries with similar scenarios. Trial registration: 47350313.9.0000.5013.

scholarly journals Development of Prediction Models for the Dengue Survivability Prediction: An Integration of Data Mining and Decision Support System

2019 ◽  
Vol 8 (10) ◽  
pp. 2199-2205
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Prediction Models ◽  
Viral Disease ◽  
Rule Based ◽  
Medical Practitioners ◽  
Tree Models

Dengue is a viral disease that has been feared by people globally. Due to its rapid prevalence and increasing threat, this study explored on the use of data mining techniques together with decision support system to develop prediction models of dengue survivability. This study was focused on three important points namely: identify significant predictor attributes to dengue survivability prediction, development of a rule-based and decision tree models for dengue survivability prediction, and the development of a dengue survivability platform for prediction purposes. The developed rule-based and decision tree models were compared according to accuracy and they underwent the 10-fold cross validation procedure and were integrated in the system to provide a platform to predict the survivability of a patient given the input medical data using a client-server configuration via the Internet. The result of the prediction for the dengue survivability may be used as an intervention by medical practitioners in the general management of dengue cases.

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