Journal of Emergency Health Care
Formerly known as: International Journal of Medical Investigation
Volume 13, Issue 4 (12-2024)
J Emerg Health Care 2024, 13(4): 106-127 |
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Zarghami S, Shirdel G H, Ghanbari M, Eskandari M R. Presentation of Algorithm Using SVD Technique to Predict Diseases. J Emerg Health Care 2024; 13 (4) :106-127
URL: http://intjmi.com/article-1-1210-en.html
URL: http://intjmi.com/article-1-1210-en.html
Ph.D. Student, Department of Mathematics, University of Qom, Qom, Iran
Abstract: (321 Views)
Background and Objective: Data mining, it is considered as knowledge discovery in data science, is the technique for patterns discovery and other valuable data from huge sets. Due to the evolution of data storage technology and the growth of big data, the use of data mining techniques has increased dramatically in the last two decades. The purpose of data mining is to transform the raw data of organizations into useful knowledge. They express the final data set and predicting the outcomes utilizing machine learning techniques. These approaches are utilized to supply data like the fraud detection and user performance, bottlenecks and even security problems.Materials and Methods: In the current study, after preparing data, disease prediction is done utilizing large matrix and data mining approaches. By investigating the new vector, it can be find out which diseases of matrix is near to this one with new signs employing the matrix rows to classify it. The study is descriptive-analytical approach which can be applicable in medical and engineering.
Results: In this research, we implemented data mining techniques using Python software to predict brain and nerve diseases.Conclusion: The technique used by Python software, the doctor enters the symptoms of the patient and the output of the program indicates 3 diseases close to the input signs for each meter, and ultimately all the meters are evaluated and the meter that has a weaker outcome is considred each time it is run. The priority of each of these meters are expressed in the article and resenting the algorithm employing the SVD approach to predict diseases that decrease the disease duration.
Results: In this research, we implemented data mining techniques using Python software to predict brain and nerve diseases.Conclusion: The technique used by Python software, the doctor enters the symptoms of the patient and the output of the program indicates 3 diseases close to the input signs for each meter, and ultimately all the meters are evaluated and the meter that has a weaker outcome is considred each time it is run. The priority of each of these meters are expressed in the article and resenting the algorithm employing the SVD approach to predict diseases that decrease the disease duration.
Keywords: Prediction Of Neurological Diseases Treatment, Treatment Methods, Diseases, Data Mining, Using SVD Technique
References
1. 1. Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M. A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches. Diagnostics. 2023 May 22;13(10):1821.
2. Peng L, Huang L, Su Q, Tian G, Chen M, Han G. LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine. Briefings in Bioinformatics. 2024 Jan 1;25(1):bbad466.
3. Sheng N, Huang L, Lu Y, Wang H, Yang L, Gao L, Xie X, Fu Y, Wang Y. Data resources and computational methods for lncRNA-disease association prediction. Computers in Biology and Medicine. 2023 Feb 1;153:106527.
4. Michael J, Aminoff Md DSc FRCP, S. 2021.Andrew Josephson S.Aminoff’s Neurology and General Medicine. 6th ed. Academic Press :1230.
5. zacharski R. 2021.A prorammers Gui to Data Mining: the ancient Art of the Numeriati. 395.
6. Falk K. 2019.Practical Recommender Systems.1st ed.USA, 432.
7. Yang HF, Phoebe Chen YF. 2015.Data mining in lung cancer pathologic staging diagnosis: Correlation between clinical and pathology information. 42(15-16): 6168-6176.
8. Yi Yeh J, His Wu T, Wei Tsao CH. 2011.Using data mining techniques to predict hospitalization of hemodialysis patients .50(2):448-439.
9. Sornalakshmi, M., Devakanth, J. J. M. A., Rajalakshmi, R., & Velmurugadass, P. (2023). An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT. Journal of Biomolecular Structure and Dynamics, 1-15.
10. Junior SB, Guido RC, Aguiar GJ, Santana EJ, Junior ML, Patil HA. Multiple voice disorders in the same individual: investigating handcrafted features, multi-label classification algorithms, and base-learners. Speech Communication. 2023 Jul 1;152:102952.
11. Zhang Y, Xiang J, Tang L, Yang J, Li J. PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm. Frontiers in Genetics. 2023 Jan 20;13:1087784.
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