Predictive models for hospital bed management using data mining techniques

Series : Advances in intelligent systems and computing, vol. 276It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient’s management and providing important information for managers to support their decisions. Throughout this study, were induced DM models capable to make predictions in a real environment using real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Three distinct techniques were considered: Decision Trees (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform classification tasks. With this work it was explored and assessed the possibility to predict the number of patient discharges using only the number and the respective date. The models developed are able to predict the number of patient discharges per week with acuity values ranging from ≈82.69% to ≈94.23%. The use of this models can contribute to improve the hospital bed management because having the discharges number it is possible forecasting the beds available for the following weeks in a determinated service

Compliance study of hazard analysis and critical control point system

Hazard Analysis and Critical Control Point (HACCP) system is based on a preventive methodology to avoid potential hazards that can cause harm and to ensure that unsafe food is not made available to consumers. This system is recognized by the Economic and Food Safety Authority, a criminal police responsible for food safety and economic inspection in Portugal. Every day, Economic and Food Safety Authority generates a large and complex volume of data from inspections and complaints, also in its classification, registration and in monitoring until the end of the process analysis. This study focuses on the reported entities that are related to non-compliance with HACCP, and tries to understand the most common infractions. Results show values between 30% and 37% related to non-compliance to HACCP. As main conclusions, from 2014 to 2018, the number of these infractions maintained the same level and it will be important to understand if the relationship between these problems are related to legislation understanding or application.info:eu-repo/semantics/publishedVersio

Using Unlabeled Data Set for Mining Knowledge from DDB

In this paper, two algorithms were introduced to describe two algorithms to describe and compare the applying of the proposed technique in the two types of the distributed database system. The First Proposed Algorithm is Homogeneous Distributed Clustering for Classification (HOMDC4C), which aim to learn a classification model from unlabeled datasets distributed homogenously over the network, this is done by building a local clustering model on the datasets distributed over three sites in the network and then build a local classification model based on labeled data that produce from clustering model. In the one computer considered as a control computer, we build a global classification model and then use this model in the future predictive. The Second Proposed Algorithm in Heterogeneous Distributed Clustering for Classification (HETDC4C) aims to build a classification model over unlabeled datasets distributed heterogeneously over sites of the network, the datasets in this algorithm collected in one central computer and then build the clustering model and then classification model. The objective of this work is to use the unlabeled data to introduce a set of labeled data that are useful for build a classification model that can predict any unlabeled instance based on that classification model. This was done by using the Clustering for Classification technique. Then presented this technique in distributed database environment to reduce the execution time and storage space that is required

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services

Big Data Attributes and Knowledge Discovery Process: An Empirical Analysis of the Anticipated Mediating Role of Cloud Computing

This study attempt to investigate whether cloud computing can act as a facilitating agent in support of knowledge discovery from big data. The study proposed a number of propositions to investigate this possible effect and impact between computing cloud, big data and knowledge discovery.  The telecommunication industry was selected as the research population and the study sample covered the main leading telecommunication companies in Jordan. A survey questionnaire was developed and distributed to a selected study sample. The proposed models of the study were tested using factor analysis and PLS method.  The results indicated that there is no mediation effect as proposed by the research model between cloud computing characteristics and knowledge discovery processes via could computing.  The results also revealed that big data attributes has a direct significate impact on a selected knowledge discovery processes and a selected cloud computing characteristics. The study also advised on some interesting finding on the three domains of the study. Keywords: Cloud computing characteristics, big data attributes, knowledge discovery processes in database, PLS method.

Computational complexity analysis of decision tree algorithms

YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets

Computational complexity analysis of decision tree algorithms

YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets

Perbandingan Algoritma Random Forest, Decision Stump, Naïve Bayes, Bayesian Network dan Algoritma C4.5 Untuk Prediksi Pola Kartu Poker

Poker merupakan salah satu permainan terpopuler didunia dengan kombinasi kartu tangan lebih dari ratusan juta. Karena jumlah yang banyak ini, pemain poker sulit untuk mengambil keputusan yang akurat. Tujuan dari penelitian ini adalah memberikan saran dari perbadingan algoritma data mining yang berbeda. Pengujian algoritma dilakukan pada algoritma C4.5, algoritma Decision Stump, algoritma Naive Bayes, algoritma Bayesian Network, serta algoritma Random Forest dengan menggunakan 25.010 data dengan 11 atribut dan melalui tahap cross-validation sebanyak sepuluh (10) kali