Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning

2009 ◽  
Vol 48 (03) ◽  
pp. 291-298 ◽  
Author(s):  
J. M. García-Gómez ◽  
J. Vicente ◽  
J. Sanjuán ◽  
R. de Frutos ◽  
R. Martín-Santos ◽  
...  
Keyword(s):  
Decision Support ◽  
Postpartum Depression ◽  
Decision Support System ◽  
Sensitivity And Specificity ◽  
Support System ◽  
Geometric Mean ◽  
High Sensitivity ◽  
Classification Model ◽  
Multilayer Perceptrons ◽  
Future Work

Summary Objective: The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians. Materials and Methods: Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy. Results: Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression. Conclusions: The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.

Intelligent Decision Support System for Osteoporosis Prediction

2012 ◽  
Vol 8 (1) ◽  
pp. 26-45 ◽  
Author(s):  
Walid Moudani ◽  
Ahmad Shahin ◽  
Fadi Chakik ◽  
Dima Rajab
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Solution Approach ◽  
Intelligent Decision Support System ◽  
Healthcare Data ◽  
Intelligent Decision ◽  
Intelligent Decision Support

The healthcare environment is generally perceived as being information rich yet knowledge poor. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The information technology may provide alternative approaches to Osteoporosis disease diagnosis. This study examines the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of patients that may have Osteoporosis Disease (OD) through its risk factors. The paper proposes to develop a dynamic rough sets solution approach in order to generate dynamic reduced subsets of features associated with a classification model using Random Forest (RF) decision tree to identify the osteoporosis cases. There has been no research in using the afore-mentioned algorithm for Osteoporosis patients’ prediction. The reduction of the attributes consists of enumerating dynamically the optimal subsets of the most relevant attributes by reducing the degree of complexity. An intelligent decision support system is developed for this purpose. The study population consisted of 2845 adults. The performance of the proposed model is analyzed and evaluated based on a set of benchmark techniques applied in this classification problem.

scholarly journals Building Model-Driven Decision Support System in Product Redesign Plan

Designs ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Swee Kuik ◽  
Li Diong
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Environmental Implications ◽  
Model Driven ◽  
Building Model ◽  
Product Use ◽  
Manufacturing Assembly ◽  
Operational Processes ◽  
Future Work

Product recovery strategy requires a thoughtful consideration of environmental implications of operational processes, undergone by a manufactured product in its entire product lifecycle, from stages of material processing, manufacturing, assembly, transportation, product use, product post-use and end-of-life. At the returns stream from product use stage, those parts and/or component assemblies from a used product have several disposition alternatives for recovery, such as direct reuse, remanufacture, recycle or disposal. Due to such complexity of the manufacturing processes in recovery, current decision methodologies focus on the performance measures of cost, time, waste and quality separately. In this article, an integrated decision model for used product returns stream is developed to measure the recovery of utilisation value in the aspects of cost, waste, time, and quality collectively. In addition, we proposed a model-driven decision support system (DSS) that may be useful for manufacturers in making recovery disposition alternatives. A case application was demonstrated with the use of model-driven DSS to measure recovery utilisation value for the used product disposition alternatives. Finally, the future work and contributions of this study are discussed.

A decision support system for constructing an alert classification model

2009 ◽  
Vol 36 (8) ◽  
pp. 11145-11155 ◽  
Author(s):  
Nien-Yi Jan ◽  
Shun-Chieh Lin ◽  
Shian-Shyong Tseng ◽  
Nancy P. Lin
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Classification Model

Decision Support System Development Based On Android Using Analytical Hierarchy Process (AHP) Method For Determining The Middle Small Business Development Priority In Sorong City

2019 ◽  
Vol 5 (2) ◽  
pp. 25-39
Author(s):  
Luluk Suryani ◽  
Raditya Faisal Waliulu ◽  
Ery Murniyasih
Keyword(s):  
Decision Support ◽  
Small Business ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
System Development ◽  
Business Development ◽  
Ahp Method ◽  
Small Business Development ◽  
Hierarchy Process

Usaha Kecil Menengah (UKM) adalah salah satu penggerak perekonomian suatu daerah, termasuk Kota Sorong. UKM di Kota Sorong belum berkembang secara optimal. Ada beberapa penyebab diantaranya adalah mengenai finansial, lokasi, bahan baku dan lain-lain. Untuk menyelesaikan permasalah tersebut peneliti terdorong untuk melakukan pengembangan Aplikasi yang dapat membantu menentukan prioritas UKM yang sesuai dengan kondisi pelaku usaha. Pada penelitian ini akan digunakan metode Analitycal Hierarchy Process (AHP), untuk pengambilan keputusannya. Metode AHP dipilih karena mampu menyeleksi dan menentukan alternatif terbaik dari sejumlah alternatif yang tersedia. Dalam hal ini alternatif yang dimaksudkan yaitu UKM terbaik yang dapat dipilih oleh pelaku usaha sesuai dengan kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu UKM. Aplikasi Sistem Pendukung Keputusan yang dikembangkan berbasis Android, dimana pengguna akan mudah menggunakannya sewaktu-waktu jika terjadi perubahan bobot pada kriteria atau intensitas.  Hasil akhir menunjukkan bahwa metode AHP berhasil diterapkan pada Aplikasi Penentuan Prioritas Pengembangan UKM.

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