scholarly journals Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector

2021 ◽  
Vol 14 (1) ◽  
pp. 3
Author(s):  
Luiz Henrique A. Salazar ◽  
Valderi R. Q. Leithardt ◽  
Wemerson Delcio Parreira ◽  
Anita M. da Rocha Fernandes ◽  
Jorge Luis Victória Barbosa ◽  
...  
Keyword(s):  
Health Sector ◽  
Recall Rate ◽  
Machine Learning Techniques ◽  
Healthcare Sector ◽  
Unified Health System ◽  
Main Challenge ◽  
Learning Techniques ◽  
Patient Will ◽  
General Data ◽  
The University

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law.

Predicting Patient No-Show Using Machine Learning Techniques in the Healthcare Sector

2021 ◽  
Author(s):  
Luiz Salazar ◽  
Valderi Reis Quietinho Leithardt ◽  
Wemerson Delcio Parreira ◽  
Anita M. R. Fernandes ◽  
Jorge Luis Victória Barbosa ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Recall Rate ◽  
Health Centers ◽  
Machine Learning Techniques ◽  
Healthcare Sector ◽  
Learning Techniques ◽  
Patient Will ◽  
Critical Problems ◽  
The University

Today, across the most critical problems faced by hospitals and health centers are those caused by the existence of patients who do not attend their appointments. Among others, this practice generates waste of resources and increases the patients’ waiting list. To handle these problems, hospitals are actively trying to implement methods to reduce the idle time caused by patient no-shows. Many scheduling systems developed require predicting whether a patient will show up for an appointment or not. Although, a challenging problem resides in obtaining these estimates precisely. The goal of this work is to analyze how objective factors influence a patient not to attending their appointment, to identify the main causes that contribute to a patient’s decision, and to be able to predict whether or not the patient will attend the scheduled appointment. As a result, the obtained model is tested on a real dataset collected in a health center linked to the University of Vale do Itajaí (UNIVALI), which includes 25 features and about 5000 samples. The algorithm that produced the best results for the available dataset is the Random Forest classifier. It reveals the best recall rate (0.91), since it measures the ability of a classifier to find all the positive instances and achieves a receiver operating characteristic curve rate of 0.969.

scholarly journals Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students

2019 ◽  
Vol 8 (11S) ◽  
pp. 1265-1267
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Evaluation Process ◽  
Education Institution ◽  
Drop Out ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Current Program ◽  
The Right ◽  
The University

Predicting the academic performance of students has been an important research topic in the Educational field. The main aim of a higher education institution is to provide quality education for students. One way to accomplish a higher level of quality of education is by predicting student’s academic performance and there by taking earlyre- medial actions to improve the same. This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. The system first accepts the performance parameters of the basic level courses which the student had already passed as these parameters also influence the further study. To pre- dict the performance of the current program, the system continuously accepts the academic performance parame- ters after each academic evaluation process. The system employs machine learning techniques to study the aca- demic performance of the students after each evaluation process. The system also learns the basic rules followed by the University for assessing the students. Based on the present performance of the students, the system classifies the students into different levels and identify the students at high risk. Earlier prediction can help the students to adopt suitable measures in advance to improve the per for- man ce. The systems can also identify the factor saffecting the performance of the same students which helps them to take remedial measures in advance.

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

scholarly journals Preliminary Attempt to Predict Risk of Invasive Pulmonary Aspergillosis in Patients with Influenza: Decision Trees May Help?

Antibiotics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 644
Author(s):  
Valeria Bellelli ◽  
Guido Siccardi ◽  
Livia Conte ◽  
Luigi Celani ◽  
Elena Congeduti ◽  
...  
Keyword(s):  
Decision Trees ◽  
Small Sample Size ◽  
Invasive Pulmonary Aspergillosis ◽  
Small Sample ◽  
University Hospital ◽  
Machine Learning Techniques ◽  
Pulmonary Aspergillosis ◽  
Vulnerable Patients ◽  
Learning Techniques ◽  
The University

Invasive pulmonary aspergillosis (IPA) is typically considered a disease of immunocompromised patients, but, recently, many cases have been reported in patients without typical risk factors. The aim of our study is to develop a risk predictive model for IPA through machine learning techniques (decision trees) in patients with influenza. We conducted a retrospective observational study analyzing data regarding patients diagnosed with influenza hospitalized at the University Hospital “Umberto I” of Rome during the 2018-2019 season. We collected five IPA cases out of 77 influenza patients. Although the small sample size is a limit, the most vulnerable patients among the influenza-infected population seem to be those with evidence of lymphocytopenia and those that received corticosteroid therapy.

scholarly journals BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4123 ◽  
Author(s):  
Jamal Toutouh ◽  
Javier Arellano ◽  
Enrique Alba
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Smart Cities ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Cyber Physical System ◽  
Mobility Patterns ◽  
Learning Techniques ◽  
Smart Mobility ◽  
The University

This article develops the design, installation, exploitation, and final utilization of intelligent techniques, hardware, and software for understanding mobility in a modern city. We focus on a smart-campus initiative in the University of Malaga as the scenario for building this cyber–physical system at a low cost, and then present the details of a new proposed evolutionary algorithm used for better training machine-learning techniques: BiPred. We model and solve the task of reducing the size of the dataset used for learning about campus mobility. Our conclusions show an important reduction of the required data to learn mobility patterns by more than 90%, while improving (at the same time) the precision of the predictions of theapplied machine-learning method (up to 15%). All this was done along with the construction of a real system in a city, which hopefully resulted in a very comprehensive work in smart cities using sensors.

scholarly journals Decision Trees for the Early Identification of University Students at Risk of Desertion

2018 ◽  
Vol 7 (4.44) ◽  
pp. 51
Author(s):  
Mayra Albán ◽  
David Mauricio ◽  
. .
Keyword(s):  
University Students ◽  
Decision Trees ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Negative Effects ◽  
Tree Model ◽  
Students At Risk ◽  
Learning Techniques ◽  
The Social ◽  
The University

The student's dropout at the universities is a topic that has generated controversy in Higher Education Institutions. It has negative effects which cause problems in the social, academic and economic context of the students. One of the alternatives used to predict the dropout at the universities is the implementation of machine learning techniques such as decision trees, known as prediction models that use logical construction diagrams to characterize the behavior of students and identify early students that at in risk of leaving university. Based on a survey of 3162 students, it was possible to obtain 10 variables that have influence into the dropout, that’s why, a CHAID decision tree model is proposed that presents the 97.95% of the accuracy in the prediction of the university students’ dropout. The proposed prediction model allows the administrators of the universities developing strategies for effective intervention in order to establish actions that allow students finishing their university careers successful.  

scholarly journals Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3129 ◽  
Author(s):  
Berny Carrera ◽  
Kwanho Kim
Keyword(s):  
Solar Power ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Comparison Analysis ◽  
Weather Variables ◽  
The Past ◽  
Main Challenge ◽  
Learning Techniques ◽  
Grid Operators ◽  
Solar Electricity

Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.

scholarly journals Machine Learning and Sustainable Mobility: The Case of the University of Foggia (Italy)

2021 ◽  
Author(s):  
Giulio Mario Cappelletti ◽  
Luca Grilli ◽  
Carlo Russo ◽  
Domenico Santoro
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Sustainable Mobility ◽  
Community Members ◽  
Applied Machine Learning ◽  
Private Organization ◽  
Learning Techniques ◽  
New Students ◽  
The University

Abstract Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a certain phenomenon. In this paper, after having analyzed data relating to the mobility habits of University of Foggia (UniFG) community members and deter- mined their emissions of pollutants, we applied machine-learning techniques to these data to estimate the quantities of pollutants (in a certain time period) produced by new subjects not present in the data sets, using very little information. In this way, we developed a method that the university could apply to inform new students about what their emissions of pollutants could be in the near future, through several easily obtainable features. This method could allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging the use of methods that are as appropriate as possible to the users’ needs. In addition, any public/private organization outside the academic environment can use the method, due to the need for little information.

scholarly journals Road Infrastructure Heritage: From Scan to InfraBIM

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Machine Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
3D Model ◽  
Machine Learning Techniques ◽  
Design Models ◽  
Learning Techniques ◽  
Historical Moment ◽  
The University ◽  
New Buildings

BIM is a methodology applied to the realization of design models applied to new buildings. To date,however, most of the building interventions as it happens in the field of cultural heritage are developed in theexisting. For this reason, scan to BIM procedures are improved and improved every day to make the use ofBIM easier. This document will describe the combination of different geomatics techniques used by theGeomatics Laboratory of the University of the Mediterranean in Reggio Calabria to create a BuildingInformation Model of a highway viaduct (infraBIM). In particular, we paid more attention to the scan to BIMphase through the segmentation of the point cloud using machine learning techniques that allow to obtain theconstitutive parametric elements of the 3D model. The model containing the geometric and physical data madeavailable by the ANAS management body in order to use the potential of infraBIM. This methodology today isof particular importance for the control, monitoring, intervention, and maintenance of road infrastructures,optimizing the procedures existing up to now. The advantages would be even more evident considering that weare living in a particular historical moment, in which a large number of bridges and viaducts in our nation aresubject to advanced forms of degradation

scholarly journals Analysis Of Heart Disease Using Machine Learning Techniques

2021 ◽  
Vol 12 (6) ◽  
pp. 32-37
Author(s):  
Samson Cherlapally Et.al
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Exchange ◽  
Data Extraction ◽  
Health Sector ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Using Data

There is a very large number in the health sector and special methods are also used systematically. Data exchange is one of the most commonly used methods. Heart disease is one of the leading causes of death in the world. This system predicts the possibility of heart disease. The results of this system provide a 100% risk of heart disease. The data used are categorized according to medical criteria. The system evaluates these parameters using data extraction methods used in Python using two basic machine learning algorithms, the Solution Tree Algorithm, and the algorithm that demonstrates the best accuracy in heart disease.

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