Empirical Review on Fraudulence Detection in Health Care Insurance Industries Using Machine Learning Techniques

2020 ◽  
Vol 12 (7) ◽  
pp. 590-603
Author(s):  
Jenita Mary A
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Machine Learning Techniques ◽  
Health Care Insurance ◽  
Learning Techniques

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

scholarly journals Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

scholarly journals Machine learning algorithms: a background artifact

2017 ◽  
Vol 7 (1.1) ◽  
pp. 143 ◽  
Author(s):  
J. Deepika ◽  
T. Senthil ◽  
C. Rajan ◽  
A. Surendar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Intelligence ◽  
Machine Learning Algorithm ◽  
Human History ◽  
Learning Techniques

With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.  

scholarly journals Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

2019 ◽  
Vol 8 (9) ◽  
pp. 1298 ◽  
Author(s):  
Giulia Lorenzoni ◽  
Stefano Santo Sabato ◽  
Corrado Lanera ◽  
Daniele Bottigliengo ◽  
Clara Minto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Health Care ◽  
Predictive Value ◽  
Machine Learning Techniques ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Heart Failure Patients ◽  
Learning Techniques ◽  
Using Data

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

An intrusion detection system for health-care system using machine and deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sagar Pande ◽  
Aditya Khamparia ◽  
Deepak Gupta
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Deep Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Learning Techniques

Purpose One of the important key components of health care–based system is a reliable intrusion detection system. Traditional techniques are not adequate to handle complex data. Also, the diversified intrusion techniques cannot meet current network requirements. Not only the data is getting increased but also the attacks are increasing very rapidly. Deep learning and machine learning techniques are very trending in the area of research in the area of network security. A lot of work has been done in this area by still evolutionary algorithms along with machine learning is very rarely explored. The purpose of this study is to provide novel deep learning framework for the detection of attacks. Design/methodology/approach In this paper, novel deep learning is the framework is proposed for the detection of attacks. Also, a comparison of machine learning and deep learning algorithms is provided. Findings The obtained results are more than 99% for both the data sets. Research limitations/implications The diversified intrusion techniques cannot meet current network requirements. Practical implications The data is getting increased but also the attacks are increasing very rapidly. Social implications Deep learning and machine learning techniques are very trending in the area of research in the area of network security. Originality/value Novel deep learning is the framework is proposed for the detection of attacks.

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