Abstract
The remarkable technological advancements in the health care industry have improved recently for the betterment of patients’ life and providing better clinical decisions. Applications of machine learning and data mining can change the available data to valuable information that can be used for recommending appropriate drugs by analyzing symptoms of the disease. In this work, a machine learning approach for multi-disease with drug recommendation is proposed to provide accurate drug recommendations for the patients suffering from various diseases. This approach generates appropriate recommendations for the patients suffering from cardiac, common cold, fever, obesity, optical, and ortho. Supervised machine learning approaches such as Support Vector Machine (SVM), Random Forest, Decision Tree, and K-nearest neighbors were used for generating recommendations for patients. The experimentation and evaluation of the study was carried out on a sample dataset created only for testing purpose and is not obtained from any source (medical practitioner). This experimental evaluation shows that the Random Forest classifier approach yields a very good recommendation accuracy of 96.87% than the other classifiers under comparison. Thus, the proposed approach is considered as a promising tool for reliable recommendations to the patients in the health care industry.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
https://www.healthit.gov/faq/what-electronic-health-record-ehr
Komal Kumar N, Vigneswari D, Vamsi Krishna M, Phanindra Reddy V (2019) An optimized random forest classifier for diabetes mellitus. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_67
Komal Kumar N, Vigneswari D, Kavya M, Ramya K, Lakshmi Druthi T (2018) Predicting non-small cell lung cancer: a machine learning paradigm. J Comput Theor Nanosci 15(6/7):2055–2058. https://doi.org/10.1166/jctn.2018.7406
Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36:7675–7680
Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104–116
Akbulut A, Ertugrul E, Topcu V (2018) Fetal health status prediction based on maternal clinical history using machine learning techniques. Comput Methods Programs Biomed 163:87–100
Lakshmi BN, Indumathi TS, Ravi N (2016) An hybrid approach for prediction based health monitoring in pregnant women. Procedia Technol 24:1635–1642
Bisaso KR, Anguzu GT, Karungi SA, Kiragga A, Castelnuovo B (2017) A survey of machine learning applications in HIV clinical research and care. Comput Biol Med 91:366–371
Kaur P, Sharma M, Mittal M (2018) Big data machine learning based secure healthcare framework. Procedia Comput Sci 132:1049–1059
Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inf 97:120–127
Darabi HR, Tsinis D, Zecchini K, Whitcomb WF, Liss A (2018) Forecasting mortality risk for patients admitted to intensive care units using machine learning. Procedia Comput Sci 140:306–313
Zihayat M, Ayanso A, Zhao X, Davoudi H, An A (2019) A utility-based news recommendation system. Decis Support Syst 117:14–27
Guan Y, Wei Q, Chen G (2019) Deep learning based personalized recommendation with multi-view information integration. Decis Support Syst 118:58–69
Vigneswari D, Komal Kumar N, Ganesh Raj V, Gugan A,Vikash SR (2019) Machine learning tree classifiers in predicting diabetes mellitus. In: IEEE-2019 5th international conference on advanced computing and communication systems (ICACCS), pp 84–87. https://doi.org/10.1109/icaccs.2019.8728388
Komal Kumar N, Lakshmi Tulasi R, Vigneswari D (2019) An ensemble multi-model technique for predicting chronic kidney disease. Int J Electr Comput Eng 9(2):1321–1326
Komal Kumar N, Roopa VD, Devi BAS (2018) MSO—MLP diagnostic approach for detecting DENV serotypes. Int J Pure Appl Math 118(5):1–6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Komal Kumar, N., Vigneswari, D. (2021). A Drug Recommendation System for Multi-disease in Health Care Using Machine Learning. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_1
Download citation
DOI: https://doi.org/10.1007/978-981-15-5341-7_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5340-0
Online ISBN: 978-981-15-5341-7
eBook Packages: EngineeringEngineering (R0)