Machine learning used in detection & diagnosis of human diseases

Rishu Raj; Vaishnavi Tripathi; Nitin Mishra

doi:10.53730/ijhs.v6nS3.6601

Authors

Rishu Raj
rishuraj08022000@gmail.com
School of Computer Science & Engineering Galgotias University, Gautam Buddha Nagar, India
Vaishnavi Tripathi School of Computer Science and Engineering (SCSE), Galgotias University, Gautam Buddha Nagar, India
Nitin Mishra School of Computer Science & Engineering Galgotias University, Gautam Buddha Nagar, India

Keywords:

human diseases, health care, prediction, machine learning, symptoms

Abstract

One of the most important topics in society is human health care. It looks to get the best diagnosis of a serious illness so they can get the care they need right away it is possible. Other fields, such as mathematics and computer science, are required to part of the search health as this awareness is often difficult. Function of following new trends is a challenge for these sectors, moving forward common. The actual number of new strategies makes it possible give a broad view that avoids certain features. In this case, we suggest a systematic analysis of human diseases related to machine learning. This paper focuses on existing strategies related to the growth of machine learning in which it is used diagnostics in the medical field to find interesting trends, make unimportant predictions, and assist in decision- making. People often feel it hesitation to go to the hospital or doctor or minor symptoms. However, in many places in some cases, these minor symptoms may cause serious health risks. Like online life advice is easily accessible, it can be a good start for users.

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References

Yoon, Y.; Cho, J.H.; Yoon, G. Non-obliged circulatory strain observing involving ECG and PPG for individual medical services. J. Drug Syst. 2009, 33, 261-266.

Bagga, P.; Hans, R. Applications of Mobile Agents in Healthcare Domain: A Literature Survey. Int. J. Grid Distrib. Comput. 2015, 8.

Grimson, J.; Stephens, G.; Jung, B.; Grimson, W.; Berry, D.; Pardon, S. Sharing health-care records over the Internet. IEEE Internet Comput. 2001, 5, 49–58.

Liu, D.Y.; Chen, H.L.; Yang, B.; Lv, X.E.; Li, L.N.; Liu, J. Design of an enhanced fuzzy k-nearest neighbor classifier based computer aided diagnostic system for thyroid disease. J. Med Syst. 2012, 36, 3243–3254, doi:10.1007/s10916-011-9815- x.

Soni, J.; Ansari, U.; Sharma, D.; Soni, S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int. J. Comput. Appl. 2011, 17, 43–48, doi:10.5120/2237-2860.

Pattekari, S.A.; Parveen, A. Prediction system for heart disease using Naïve Bayes. Int. J. Adv. Comput. Math. Sci. 2012, 3, 290–294.

Chaurasia, V.; Pal, S. Data mining approach to detect heart diseases. Int. J. Adv. Comput. Sci. Inf. Technol. 2013. 2, 56–66.

Vijayarani, S.; Dhayanand, S. Liver disease prediction using SVM and Naïve Bayes algorithms. Int. J. Sci. Eng. Technol. Res. 2015, 4, 816–820.

Thangaraju, P.; Mehala, R. Novel Classification based approaches over Cancer Diseases. System 2015, 4, 294–297, doi:10.17148/IJARCCE.2015.4370.

Vijayarani, S.; Dhayanand, S. Data mining classification algorithms for kidney disease prediction. Int. J. Cybern. Informatics 2015, 4, 13–25., doi:10.5121/ijci.2015.4402.

Das, D.; Ito, J.; Kadowaki, T.; Tsuda, K. An interpretable machine learning model for diagnosis of Alzheimer’s disease. PeerJ 2019, 7, doi:10.7717/peerj.6543.

Zhu, W.; Xie, L.; Han, J.; Guo, X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers 2020, 12, 603.

Tomar, D.; Agarwal, S. A survey on Data Mining approaches for Healthcare. Int. J. Bio-Sci. Bio-Technol. 2013, 5, 241–266.

Werbos, P. J.. Beyond Regression: New Tools for Prediction and Analysis in Behavioral Sciences. Ph.D. Thesis, Harvard University, Cambridge, MA, USA, 1974.

Schmidhuber, J. Deep learning in neural networks: An overview. Neural Networks 2015, 61, 85–117, doi:10.1016/j.neunet.2014.09.003.

Erb, R.J. Introduction to backpropagation neural network computation. Pharm. Res. 1993, 10, 165–170, doi:10.1023/A:1018966222807.

Alimadadi, A.; Aryal, S.; Manandhar, I.; Munroe, P.B.; Joe, B.; Cheng, X. Artificial intelligence and machine learning to fight COVID-19. Physiol. Genom. 2020, 52, 200– 202, doi:10.1152/physiolgenomics.00029.2020.