Publication Type : Journal Article
Publisher : Lecture Notes in Electrical Engineering
Source : Lecture Notes in Electrical Engineering, Springer Verlag, Volume 545, p.211-222 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065476422&doi=10.1007%2f978-981-13-5802-9_20&partnerID=40&md5=3d4dfc894d62cdad202fc3b6d81e1e3c
ISBN : 9789811358012
Keywords : Cost effectiveness, cost reduction, Cost-effective solutions, Data mining, Data warehouses, decision making, Decision trees, Deep learning, electronic medical record, Health care, health insurance, Healthcare organizations, Learning algorithms, length of stay, Machine learning, Machine learning techniques, Medical computing, Multilayer artificial neural networks, Neural networks, Predictive analytics, Random forests, Sales, Statistical methods, XGBoost
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2019
Abstract : Healthcare or health insurance agencies have been in a continuous state of change, especially during the present technology expansion. They are beneath colossal pressure to forecast customer health issue and to generate surplus premium holders which will simultaneously reduce the cost. Examining and utilizing the vast data available are critical for healthcare companies in designing various strategies in the future. Many such healthcare organizations have already moved toward data mining and analytics with data warehouse methodologies and business intelligence with statistical analysis. However, further adaptation is required, as they must use different data from new sources in a blend with the prior sources. To consider this adaptation, predictive analysis technique is proposed. Predictive analysis comprises diverse statistical methods from predictive modeling, machine learning, and data mining that analyze present and past realities to make predictions about future. There are several advantages of using descriptive and predictive analytics in healthcare domain for concrete decision making of cost-effective solutions to their customers. This paper expands upon risk mitigation tactics to foresee high-risk patients. This is done by considering clinical data that is an electronic medical record and evaluating risk associated using stacked ensemble machine learning techniques. This technique helps achieve higher predictive accuracy of 90.17% and specificity of 94.90% in identifying high-risk patients from a lesser amount of data. It has been centered on payer analytics and customer analytics in light of novel machine learning algorithm to give full-cycle knowledge toward cost reduction and improvement in nature of care. © 2019, Springer Nature Singapore Pte Ltd.
Cite this Research Publication : N. R. Jyothi and Prakash, G., “A Deep Learning-Based Stacked Generalization Method to Design Smart Healthcare Solution”, Lecture Notes in Electrical Engineering, vol. 545, pp. 211-222, 2019.