scholarly journals Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

2021 ◽  
Vol 11 (18) ◽  
pp. 8478
Author(s):  
Irum Matloob ◽  
Shoab Ahmad Khan ◽  
Farhan Hussain ◽  
Wasi Haider ◽  
Rukaiya Rukaiya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Clustering Algorithm ◽  
Insurance Industry ◽  
Cost Effective ◽  
Distribution Model ◽  
Medical Benefit ◽  
Applied Machine Learning ◽  
Healthcare Needs ◽  
Medical Benefits

The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

scholarly journals Comparison of Machine Learning Algorithms for Processing of Health Insurance Claim

2020 ◽  
Vol 5 (2) ◽  
pp. 01-19
Author(s):  
Paridhi Saxena ◽  
◽  
Abhishek Seth ◽  
Gangesh Chawla ◽  
Ranganath Singari
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Consumer Satisfaction ◽  
Insurance Industry ◽  
Machine Learning Algorithms ◽  
Insurance Companies ◽  
Insurance Claim ◽  
Lifestyle Behaviour ◽  
Customer Lifetime ◽  
Health Insurance Industry

The health insurance industry protects against financial losses resulting from various health conditions. Since a long, it has relied on statistics and data to calculate risks and thereby, centre attention more profoundly on a particular target audience for increasing the operational efficiency of the industry. Technologies like Machine Learning and Artificial Intelligence prove to be an efficient tool for enabling insurance companies to predict the Customer Lifetime Value (CLV). This can be done using customer lifestyle behaviour data allowing to assess the customer's potential profitability for insurance companies. This creates a more personalised marketing offer within the audience. The insurance industry and its components constitute a dynamic and competitive sector representing approximately 2.7 percent of the US Gross Domestic Product (GDP). As customers have become progressively scrupulous about narrowing down their specific requirements, insurers and insurance companies are scrutinizing techniques for improving business operations and consumer satisfaction. An attempt in this regard has been made to analyse the “sample insurance claim prediction dataset" using various machine learning models including Decision tree, Random Forest algorithms, Naïve Bayes, K-nearest neighbour algorithm, Supper Vector machines and Neural Networks. A comparative analysis is performed to generate reports.

scholarly journals Application of Machine Learning Techniques to Predict the Impact of Health Insurance on the Wellbeing of an Individual

2019 ◽  
Vol 9 (2) ◽  
pp. 3065-3070
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Large Scale ◽  
Insurance Industry ◽  
Financial Burden ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
The Government ◽  
The Impact

The healthcare domain in India has suffered considerably despite the advancement in technology. Several financing schemes are endorsed by the insurance companies to lessen the financial burden faced by the government and people. Nonetheless, Health Insurance segment in India remains underdeveloped due to various complexities that it faces. This paper exploits a heuristic sampling approach combined with the ensemble Machine Learning algorithms on the large-scale insurance business data to realize the current shape of the Health Insurance industry in India. Through the courtesy of Data Mining and Data Analytics, it is plausible to furnish insights that assist the common people in acquiring closure that helps in the process of decision making.

E-Insurance :A Platform For Insurance Penetration In India

2019 ◽  
Vol 118 (6) ◽  
pp. 90-93
Author(s):  
L. Terina Grazy ◽  
Dr.G. Parimalarani
Keyword(s):  
Insurance Industry ◽  
Cost Effective ◽  
Online Marketing ◽  
Insurance Companies ◽  
The Internet ◽  
Insurance Sector ◽  
The People ◽  
Cost Effective Approach ◽  
Time And Energy ◽  
Work Done

E-commerce is a part of Internet Marketing. The arrival of Internet made the world very simple and dynamic in all the areas. Internet is the growing business as a result most of the people are using it in their day to day life. E-commerce is attractive and efficient way for both buyers and sellesr as it reduce cost, time and energy for the buyer. No surprise the insurance sector has become quite active within the internet sphere. Most insurance companies are offering policies to be brought online and also the portals for paying premiums. It actually saves from hassles involved in going to an insurance office and spend hours to get the insurance work done. Insurance has become an important and crucial aspect of life. Online insurance is the best and most cost effective approach of taking the insurance deal. This paper focused on influence of online marketing on the insurance industry in India, usage of internet in India , the internet penetration in India and the online sale of insurance product by the insurance sector.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Participatory and responsive governance in universal health coverage: an analysis of legislative provisions in Thailand

2021 ◽  
Vol 6 (2) ◽  
pp. e004117
Author(s):  
Aniqa Islam Marshall ◽  
Kanang Kantamaturapoj ◽  
Kamonwan Kiewnin ◽  
Somtanuek Chotchoungchatchai ◽  
Walaiporn Patcharanarumol ◽  
...  
Keyword(s):  
Health Insurance ◽  
Social Health Insurance ◽  
Universal Coverage ◽  
Civil Servant ◽  
Social Health ◽  
Health Coverage ◽  
Medical Benefit ◽  
The Social ◽  
Universal Health ◽  
Universal Coverage Scheme

Participatory and responsive governance in universal health coverage (UHC) systems synergistically ensure the needs of citizens are protected and met. In Thailand, UHC constitutes of three public insurance schemes: Civil Servant Medical Benefit Scheme, Social Health Insurance and Universal Coverage Scheme. Each scheme is governed through individual laws. This study aimed to identify, analyse and compare the legislative provisions related to participatory and responsive governance within the three public health insurance schemes and draw lessons that can be useful for other low-income and middle-income countries in their legislative process for UHC. The legislative provisions in each policy document were analysed using a conceptual framework derived from key literature. The results found that overall the UHC legislative provisions promote citizen representation and involvement in UHC governance, implementation and management, support citizens’ ability to voice concerns and improve UHC, protect citizens’ access to information as well as ensure access to and provision of quality care. Participatory governance is legislated in 33 sections, of which 23 are in the Universal Coverage Scheme, 4 in the Social Health Insurance and none in the Civil Servant Medical Benefit Scheme. Responsive governance is legislated in 24 sections, of which 18 are in the Universal Coverage Scheme, 2 in the Social Health Insurance and 4 in the Civil Servant Medical Benefit Scheme. Therefore, while several legislative provisions on both participatory and responsive governance exist in the Thai UHC, not all schemes equally bolster citizen participation and government responsiveness. In addition, as legislations are merely enabling factors, adequate implementation capacity and commitment to the legislative provisions are equally important.

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

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