Health Care Chat-Bot Using Artificial Intelligence and Machine Learning

Mohan Kumar B K; Praveen S R; Mahesh R; Manoj Gowda M N; Dhanush T C

doi:10.32628/IJSRCSEIT

Authors

Mohan Kumar B K U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Praveen S R Assistant Professor, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Mahesh R U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Manoj Gowda M N U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Dhanush T C U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author

DOI:

https://doi.org/10.32628/IJSRCSEIT

Keywords:

Chat Bots, AI, ML

Abstract

Automatized medical chat bots are conversationally built with technology in mind with having the potential to reduce efforts to healthcare cost and improve access to medical services and knowledge. We build a diagnosis bot that engages patients in the conversation for their medical queries. Our chatbot system is qualified to identify symptoms from user input with a standard prediction of 65% - 70%. We have critically and exhaustively analysed recent research and review papers that a pertinent to AI based health care chatbots. Based on the basic concepts used in their mechanisms, the existing approaches are categorized. The emphasis is on the concept used by the concerned authors, the methodology used for experimentations and the performance evaluation parameters. The claims of the researchers are also highlighted. Our findings from the exhaustive literature review are mentioned along with the identified problems. This project is very important for the comparative study of various healthcare chatbots approaches which is prerequisite for solving remote health issues. In the end, we’ve proposed our own chatbot pertaining to the health care system. We have blended AI with ML. We have implemented the idea wherein one can easily and readily diagnose the disease or illness and the details can be provided remotely before consulting a medical practitioner or visiting a doctor.

Downloads

Download data is not yet available.

References

Mohiuddin A, Abdun NM, Jiankun H. Outlier detec- tion. In The State of the Art in Intrusion Prevention and Detection, Al-Sakib Khan Pathan (ed). Chapter: 1, Publisher: CRC Press: New York, USA, 2014. DOI: 10.1201/b16390-3

J. Kumaraswamy, Anil K C, T R Veena, G. Purushotham, Sunil Kumar K, “Investigating the Mechanical Properties of Al 7075 Alloy for Automotive Applications: Synthesis and Analysis” in Scopus indexed EVERGREEN Journal with ISSN: 2189-0420, Vol. 10, Issue 03, pp.1286-1295, September 2023.

Bilge L, Balzarotti D, Robertson W, Kirda E, Kruegel C. Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis. Proceed- ings of the 28th Annual Computer Security Applica- tions Conference. 2012, 129–138

Kumaraswamy, J., Anil, K.C., Canbay, C.A., N D Shiva Kumar. Electro-Whirling Stir Casting: a Novel Approach for Fabricating Al7075/SiC MMCs with Enhanced Thermal Characteristics. Silicon. https://doi.org/10.1007/s12633-023-02678-y

Münz G, Li S, Carle G. Traffic anomaly detection using k-means clustering. In Proceedings of Performance, Reliability and Dependability Evaluation of Communi- cation Networks and Distributed Systems, 4 GI / ITG Workshop MMBnet. Hamburg, Germany. 2007

Kumaraswamy Jayappa, Kyathasandra Chikkanna Anil, Zulfiqar A. Khan, Enhancing wear resistance in Al-7075 composites through conventional mixing and casting techniques, Journal of Materials Research and Technology, Volume 27, 2023, pp. 7935-7945. https://doi.org/10.1016/j.jmrt.2023.11.171.

Hofstede R, Bartos V, Sperotto A, Pras A. Towards real-time intrusion detection for NetFlow and IPFIX. In: 9th International Conference on Network and Ser- vice Management, CNSM 2013, October 2013, Zürich, Switzerland. 2013, 14–18

Kumaraswamy, J., Anil, K. C., Veena, T. R., Reddy, M., & Sunil Kumar, K. (2023). Influence of particulates on microstructure, Mechanical and Fractured behaviour on Al-7075 alloy composite by FEA. Australian Journal of Mechanical Engineering, 1–15. https://doi.org/10.1080/14484846.2023.2276987

Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the Third SIAM International Conference on Data Mining. 2003

J. Kumaraswamy et al., "Thermal Analysis of Ni-Cu Alloy Nanocomposites Processed by Sand Mold Casting," Advances in Materials Science and Engineering, vol. 2022, Article ID 2530707, 11 pages, 2022. https://doi.org/10.1155/2022/2530707.

Gogoi P, Bhattacharyya DK, Borah B, Kalita JK. A survey of outlier detection methods in network anom- aly identification. The Computer Journal 2011; 54(4):570–588.

]. J. Kumaraswamy, K.C. Anil and V. Shetty, Development of Ni-Cu based alloy hybrid composites through induction furnace casting, Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2022.09.215

Chandola V, Banerjee A, Kumar V. Anomaly detec- tion: a survey. ACM Computing Surveys (CSUR) 2009; 41(3):15–58.

Anil Kyathasandra Chikkanna, Kuchangi Venkatappa Manjunath, Kumaraswamy Jayappa, Mahadeva Reddy, Akash Biradar, Effect of Chilling & B4C content on Machining Efficiency and Surface Quality in Wire-Cut Machining of Aluminum Matrix Chilled Composites, Mechanics of Advanced Composite Structures, Volume 11, Issue 2 Pages 341-350. https://doi.org/10.22075/macs.2024.31090.1528

Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX. 2000, 93–104.

Garšva E, Paulauskas N, Gražulevičius G, Gulbinovič L. Packet inter-arrival time distribution in academic computer network. Elektronika ir elektrotechnika. Elec- tronics and Electrical Engineering 2014; 20(3):87–90.