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Doctor Recommendation and Appointment System

Md. Lutful Islam1 , Khalid Alam2 , Ashfi Ansari3 , Kashyap Godambe4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 610-613, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.610613

Online published on Feb 28, 2019

Copyright © Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe, “Doctor Recommendation and Appointment System,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.610-613, 2019.

MLA Style Citation: Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe "Doctor Recommendation and Appointment System." International Journal of Computer Sciences and Engineering 7.2 (2019): 610-613.

APA Style Citation: Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe, (2019). Doctor Recommendation and Appointment System. International Journal of Computer Sciences and Engineering, 7(2), 610-613.

BibTex Style Citation:
@article{Islam_2019,
author = {Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe},
title = {Doctor Recommendation and Appointment System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {610-613},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3713},
doi = {https://doi.org/10.26438/ijcse/v7i2.610613}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.610613}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3713
TI - Doctor Recommendation and Appointment System
T2 - International Journal of Computer Sciences and Engineering
AU - Md. Lutful Islam, Khalid Alam, Ashfi Ansari, Kashyap Godambe
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 610-613
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition. A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making. Machine learning helps in data-driven decision making, identification of key trends and driving research efficiency. When it comes to healthcare, there are different ways in which machine learning techniques can be applied for effective diseases prediction, diagnosis, and treatments, improving the overall operations of healthcare. Effective machine learning implementation enables healthcare professionals in better decision-making, identifying trends and innovations, and improving the efficiency of research and clinical trials

Key-Words / Index Term

Doctor, Symptoms, User, Patient, Machine Learning, Healthcare, Prediction, Location, Diseases

References

[1] Fetter, R. B., & Thompson J. D., “PATIENTS` WAITING TIME AND DOCTORS` IDLE TIME IN THE OUTPATIENT SETTING”, Health Serv ice Research, Patent Number US PMC1067302, Patent Date Jan. 26, 2012.FL (US) 66-90.
[2] Meherwar Fatima, Maruf Pasha, “SURVEY OF MACHINE LEARNING ALGORITHMS FOR DISEASE DIAGNOSTIC”, ICIW 2012: Department of Information Technology, Bahauddin Zakariya University, Multan, Pakistan
[3] D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, G. Escobar, “BIG DATA IN HEALTH CARE: USING ANALYTICS TO IDENTIFY AND MANAGE HIGH-RISK AND HIGH-COST PATIENTS”, Health Affairs, vol. 33, no. 7., Patent No. US 1123-1131A1, Patent Date Jul.19, 2014.
[4] Hylton, A. & Suresh, S., “APPLICATION OF INTELLIGENT AGENTS IN HOSPITAL APPOINTMENT SCHEDULING SYSTEM”, International Journal of Computer Theory and Engineering, Vol. 4, No. 4, April 2012, ISSN: 625-630.
[5] B. Qian, X. Wang, N. Cao, H. Li, Y.-G. Jiang, “A RELATIVE SIMILARITY BASED METHOD FOR INTERACTIVE PATIENT RISK PREDICTION”, Data Mining Knowl. Discovery, vol. 29, no. 4, PP:1070-1093, 2015.
[6] S. Marcoon, A. M. Chang, B. Lee, R. Salhi, J. E. Hollander,, “HEART SCORE TO FURTHER RISK STRATIFY PATIENTS WITH LOW TIMI SCORES”, Critical Pathways Cardiol., vol. 12, no. 1, pp. 1-5, 2013.
[7] M. Chen, S. Mao, Y. Liu, “BIG DATA: A SURVEY”, Mobile Netw. Appl., vol. 19, pp. 171-209, Apr. 2014.
[8] P. Groves, B. Kayyali, D. Knott, S. van Kuiken, “THE‘BIG DATA’REVOLUTION IN HEALTHCARE”, Accelerating Value and Innovation, 2016.