Healthcare based financial decision making system using artificial intelligence

https://doi.org/10.53730/ijhs.v6nS2.8025

Authors

  • Neha Nandal Associate Professor, Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad
  • Anupam Singh School of Computer Science, University of Petroleum and Energy Studies, Dehradun
  • Manoj Kumar Department of Computer Science & Technology, Manav Rachna University, Faridabad, India
  • Rohit Tanwar School of Computer Science, University of Petroleum and Energy Studies, Dehradun

Keywords:

artificial intelligence, healthcare, neural network, LSTM approach, decision system

Abstract

Artificial Intelligence is providing immense areas to work with and areas like Deep Learning and Machine Learning is taking over many research areas nowadays. The analysis and prediction of time series with machine and deep learning techniques are providing very promising results in the field of healthcare. The future values can be predicted with the help of time series. Therefore, the prediction of time series in healthcare based financial management provides organization with the useful information that supports in decision making. In this paper, the time series prediction on healthcare financial data is done by implementing Long Short Term Memory approach of Neural Networks for prediction of output for the time series data to predict business capabilities. Temporal characteristics of healthcare financial data are analyzed for time series forecasting. From the results, it is evident that this model is highly feasible to analyze the data with high precision and accuracy.

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References

Aarshay Jain, “A comprehensive beginner’s guide to create a Time Series Forecast”, Available: https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes- python/, July 2019.

Christopher Olah, “Understanding LSTM Networks”, Available: https://colah.github.io/posts/2015-08-Understanding-LSTMs/, July 2019.

Y. Kara, M. Acar Boyacioglu, and Ö. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange,” Expert Syst. Appl., vol. 38, no. 5, pp. 5311–5319, May 2011.

“An End-to-End Project on Time Series Analysis and Forecasting with Python.” [Online]. Available: https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis- and-forecasting-with-python-4835e6bf050b. [Accessed: 20-Jun-2020].

S. I. Lee and S. J. Seong Joon Yoo “A Deep Efficient Frontier Method for Optimal Investments | Semantic Scholar.” [Online]. Available: https://www.semanticscholar.org/paper/A-Deep-Efficient-Frontier-Method-for-Optimal-Lee- Yoo/26059cc17e3349ec50631e0ec2f2e02ed00fb0b6. [Accessed: 20-Jun-2020].

M. Ivanovic and V. Kurbalija, “Time series analysis and possible applications,” in 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 - Proceedings, 2016, pp. 473–479.

M. Qi and G. P. Zhang, “Trend time series modeling and forecasting with neural networks,” in IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr), 2003, vol. 2003-January, pp. 331–337.

J. Cristian and B. Gamboa “Deep Learning for Time-Series Analysis.” [Online]. Available: https://www.researchgate.net/publication/312170098_Deep_Learning_for_Time- Series_Analysis. [Accessed: 20-Jun-2020].

J. F. Chen, W. L. Chen, C. P. Huang, S. H. Huang, and A. P. Chen, “Financial time-series data analysis using deep convolutional neural networks,” in Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016, 2017, pp. 87–92.

Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal, “Long Short Term Memory Networks for Anomaly Detection in Time Series,” 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Available:https://www.researchgate.net/publication/304782562_Long_Short_Term_Memory_Networks_for_Anomaly_Detection_in_Time_Series. [Accessed 7 9 2019].

M. A. Ghazanfar, S. A. Alahmari, Y. F. Aldhafiri, A. Mustaqeem, M. Maqsood, and M. A. Azam, “Using machine learning classifiers to predict stock exchange index,” Int. J. Mach. Learn. Comput., vol. 7, no. 2, pp. 24–29, Apr. 2017.

J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, Jan. 2015.

Sathiyamoorthi, V., Ilavarasi, A. K., Murugeswari, K., Ahmed, S. T., Devi, B. A., & Kalipindi, M. (2021). A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images. Measurement, 171, 108838.

Z. Li and V. Tam, “A comparative study of a recurrent neural network and support vector machine for predicting price movements of stocks of different volatilites,” in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018, vol. 2018-January, pp. 1–8.

J. Ali Khan, “Predicting Trend in Stock Market Exchange Using Machine Learning Classifiers,” Sci.Int.(Lahore), vol. 28, no. 2, pp. 1363–1367, 2016.

Box, G. Jenkins, "Time Series Analysis: Forecasting and Control", San Francisco: Holden- Day, 1970.

C. Krauss, X. A. Do, N. Huck, " Deep neural networks, gradientboosted trees, random forests: Statistical arbitrage on the S&P 500", FAU Discussion Papers in Economics 03/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, 2016.

A. Kulkarni, " What the heck is time series data and why do I need a time series database", Available: https://blog.timescale.com/blog/what-the-heck-is-time-series-data-and-why-do-i- need-a-time-series-database-dcf3b1b18563/. July 2019.

R. J. Hyndman,G. Athanasopouls "Forecasting: principles and practice", Retrieved from http://otexts.org/fpp/, July 2013.

R. J. Hyndman,A. B. Koehler, J. K. Ord, R. D. Snyder, "Forecasting with Exponential Smoothing, Springer, July 2013.

G. Box, E. P. George, G. M. Jenkins, " Time Series Analysis: Forecasting and Control", 3rd ed. Upper Saddle River, NJ: Prentice Hall, 1994.

C. W. J. Granger, " Forecasting in Business and Economics ", 2nd ed. Boston: Academic Press, 1989.

Hamilton, D. James, " Time Series Analysis ", Princeton, NJ: Princeton University Press, 1994.

Harvey, C. Andrew, "Time Series Models", 2nd ed. Cambridge, MA: MIT Press, 1993.

R. S. Pindvck, D. L. Rubinfield, " Econometric Models and Economic Forecasts ", 3rd ed. New York: McGraw-Hill College Div., 1997.

Published

28-05-2022

How to Cite

Nandal, N., Singh, A., Kumar, M., & Tanwar, R. (2022). Healthcare based financial decision making system using artificial intelligence. International Journal of Health Sciences, 6(S2), 11255–11267. https://doi.org/10.53730/ijhs.v6nS2.8025

Issue

Section

Peer Review Articles