Machine Learning Algorithms and Modalities in Enhancing Healthcare Decisions
Keywords:
Support Vector Machine, Artificial Neural Network, Healthcare, ML, Artificial Intelligence, Deep Learning
Abstract
Management information system (MIS), decision support system (DSS), and executive support system (EES) are the inevitable constituents of the intelligent systems which are being integrated with the infrastructural and technological development of the organizations to address non-routine decisions. The intelligent systems are incorporated with methodologies that support providing solutions to unpredicted decisions by employing mathematical and statistical tools and incorporating software programs embedded with cutting-edge algorithms. We investigate the applicability of several algorithms in the healthcare domain and propose mechanisms of development of machine learning techniques in the area of artificial intelligence. Artificial intelligence (AI) encompasses integer linear programming (ILP) and machine learning (ML) that further motivates us to dig up the algorithms and learning techniques to find the best solution in the field of predictive analytics for the supervised learning environments in correlating blood glucose concentration and hematocrit volume.
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[2] K. C. Laudon and J. P. Laudon, “Management Information System: Managing the Digital Firm,” Fifth Edition, Pearson Education Limited, 2018.
[3] W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Neural Computation, Vol. 29, No. 9, pp. 2352-2449, 2017.
[4] A. Storkey, “Increasing the capacity of a Hopfield Network without sacrificing functionality,” Proceedings of the 7th Int. Conf. on Artificial Neural Networks, Springer London, 1997.
[5] M.V. Marino, G.Shabat, G.Gulotta, A.L. Komorowski , “From Illusion to Reality: A Brief History of Robotic Surgery,” Surgical Innovation, Vol. 25, No.3, pp. 291-296, 2018.
[6] Ajmal Zemmar, Andres M. Lozano, and Bradley J. Nelson, “The rise of robots in surgical environments during COVID-19,” Nature Machine Intelligence, Vol.2, pp. 566–572, 2020.
[7] Paduri Veerabhadram, “Applications of Robotics in medicine,” International journal of scientific & engineering research, Vol. 2, No. 8, 2011.
[8] Iulia Andreas,Elio Mazzone,Fijs W.B Van Leeuwan et al., “AI and robotics: a combination that is changing the operating room,” World journal of Urology , Vol.38, pp.2359-2366, 2020.
[9] Mahendra Bhandari, Trevor Zeffiro, Madhu Reddiboina, “AI and robotic surgery: current perspective and future directions,” Current Opinion in Urology, Vol.30, No.1 1, pp. 48-54, 2020.
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[11] B. Zhao, R.S.Walterman, R.D.Urman, R.A.Gabriel, “A ML approach to predicting case duration for robot-assisted surgery” , Journal of Medical Systems, Vol. 43, No. 32 , 2019.
[12] C.Singh, J. Singh, “Biomedical Signal Processing, Artificial Neural Network: A Review,” Indian Journal of Science and Technology, Vol.9, No. 47, 2016.
[13] C.Peel, “The cardiopulmonary system and movement dysfunction,” Physical Therapy, Vol.76, No. 5, 1996.
[14] Noura AlHinai, “Chapter 1 - Introduction to biomedical signal processing and AI,” Dr.Ilker Ozsahin, Dr.Dilber Uzun Ozsahin contd. below
[15] “Chapter 7 - Neural network applications in medicine,” Editor(s): Walid Zgallai, in Developments in Biomedical Engineering and Bioelectronics, Biomedical Signal Processing and AI in Healthcare, Academic Press, pp. 1-28, 2020.
[16] W.Yang, Y. Si, D. Wang, B. Guo, “Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine,” Computers in Biology and Medicine, Vol.101, pp. 22-32, 2018.
[17] Chandan Kumar Jha, Maheshkumar H. Kolekar, “Cardiac arrhythmia classification using tunable Q-wavelet transform-based features and support vector machine classifier,” Biomedical Signal Processing and Control, Vol. 59, 2020.
[18] R. Rodriguez, O. O. Vergara Villegas, V. G. Cruz Sanchez, J. Bila, and A. Mexicano, “Arrhythmia disease classification using a higher-order neural unit,” Fourth International Conference on Future Generation Communication Technology (FGCT), pp. 1-6, 2015.
[19] Khaled Mohamad Almustafa, “Classification of epileptic seizure dataset using different ML algorithms,” Informatics in Medicine Unlocked, Vol. 21, 2020.
[20] C. W. Yean et al., “Analysis of The Distance Metrics of KNN Classifier for EEG Signal in Stroke Patients,” Proceedings of International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), pp. 1-4, 2018.
[21] A. Shoeibi, D. Sadeghi, P. Moridian et al., “Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models,” Frontiers in Neuroinformatics, Vol.15, 2021.
[22] P. J. Lisboa and A.F.G. Taktak, “The use of artificial neural networks in decision support in cancer: A systematic review,” Neural Networks, Vol.19, No. 4, pp. 408–415, 2006.
[23] K. Doi, “Computer-aided diagnosis in medical imaging: historical review, current status and future potential,” Computerized Medical Imaging and Graphics, Vol.31, No. 4-5, pp. 198-211, 2007.
[24] A. Papadopoulossa, D.I. Fotiadisb, A. Likasb, “An Automatic Microcalcification Detection System Based on a Hybrid Neural Network Classifier,” AI in Medicine, Vol. 25, pp. 149–167, 2002.
[25] B. Verma, P. McLeod, A. Klevansky, “A novel soft cluster neural network for the classification of suspicious areas in digital mammograms,” Pattern Recognition, Vol.42, No. 9, pp. 1845- 1852, 2009.
[26] Z. H. Zhou, Y. Jiang, Y. B. Yang, S. F. Chen, “Lung cancer cell identification based on artificial neural network ensembles,” AI in Medicine, Vol.24, No. 1, pp. 25-36, 2002.
[27] E. I. Mohamed, C. Maiolo, R. Linder, S. J. Poppl, and A. De Lorenzo, “Artificial neural network analysis: a novel application for predicting site-specific bone mineral density,” Acta Diabetologica, Vol. 40, pp. S19-S22, 2003.
[28] G. Pan, G. Yan, X. Song and X. Qiu, “BP neural network classification for bleeding detection in wireless capsule endoscopy,” J. Medical Engineering and Techno., Vol.33, No. 7, pp. 575- 581, 2009.
[29] L. Sinnenberg, A.M.Buttenheim, K. Padrez, C. Mancheno, L. Ungar, R.M. Merchant, “Twitter as a tool for health research: a systematic review,” Health, Vol.107, No.1, pp. e1-e8, 2017.
[30] V. M. Castro, J. Minnier, S. N. Murphy , I. Kohane, S. E. Churchill, G. Gainer, “Validation of electronic health record phenotyping of bipolar disorder cases and controls,” American Journal of Psychiatry, Vol. 172, No. 4, pp. 363-372, 201
[31] N.J. Carson, “Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and ML of electronic health records,” PLoS One, Vol. 14 No. 2, pp. 01-14, 2019.
[32] G. Bedi, F. Carrillo, G.A. Cecchi, D.Fernandez Slezak, M. Sigman , N.B. Mota, et al., “Automated analysis of free speech predicts psychosis onset in high-risk youths,” NPJ Schizophrenia, Vol. 1, 2015.
[33] T. Roy, J. McClendon, L. Hodges., “Analyzing Abusive Text Messages to Detect Digital Dating Abuse”, in IEEE International Conference on Healthcare Informatics. 2018 Presented at: IEEE 18; March 2-3, 2018; New York.
[34] Lior Rokach Ben-Gurion University of the Negev, Israel Oded Maimon Tel-Aviv University, Israel, “Data Mining with Decision Trees:Theory and Applications, Series in Machine Perception and Artificial Intelligence: Volume 81,” 2nd Edition, Published by World Scientific Publishing Co. Pte. Ltd.
[35] Andriy Burkov, “The Hundred-Page ML Book” Published by Andriy Burkov, 2019.
[36] G. David, “Logistic Regression: Binary and Multinominal,” 2014 Edition, by Garson and Statistical Associates Publishing.
[37] Suman Pandey, Pawan K. Tiwari, and Tae-Young Byun, “Integration of Bio-Sensing with Information and Communication to Provide Improved Health-Care Services,” Lecture Notes in Electrical Engineering, Vol. 181, 2012.
[38] Pawan K. Tiwari, Hakhyun Nam, and Young Soo Sohn, “Correlation between Blood Glucose and Hematocrit: A New Estimation Methodology,” BioChip Journal, Vol. 6, pp. 206-212, 2012.
Published
2022-06-01
How to Cite
Tiwari, P., Lee, Y., Johny, G., Gaurav, T., Pandey, R., Choudhury, S. R., Sharma, K., & Pandey, S. (2022). Machine Learning Algorithms and Modalities in Enhancing Healthcare Decisions. Majlesi Journal of Mechatronic Systems, 11(2), 7-14. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/523
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