Applications of Machine Learning in The Field of medical care
ABSTRACT
These years, with
artificial intelligence and machine learning becoming the hotspot of research,
several applications have emerged in each of these areas. It exists not only as
a kind of academic frontier but also something close to our life. In this
trend, the combination of medical care and machine learning becomes more and
more tighter. The proposal of its main idea also greatly alleviated the
existing situation of unbalanced medical distribution and resources strain.
This paper summarizes some application of machine learning and auxiliary tumor
treatment in the process of medical resource allocation, and puts forward some
new methods of application to realize it closer to human life in the era of
artificial intelligence and the explores a good situation of mutual combination
of medical industry and computer industry, which is benefit both.
EXISTING SYSTEM:
In present systems
there is hardly any medical service available in remote locations. Persons
needing medical services often need to travel long distances. Even in urban
areas the service is sometimes not available immediately. Patients and doctors
are hardly to communicate with each others.And also patients had to wait for
long time in order to communicate to the doctor.This main concern has to do with the confidentiality of the
data. There is also concern about non-confidential data however such Systems
that deal with these transfers are often referred to as Health Information
Exchange.
DISADVANTEGES:
Ø Data
Acquisition. Machine Learning requires massive data sets to
train on, and these should be inclusive/unbiased, and of good quality. ...
Ø Time and Resources.
...
Ø Interpretation of
Results. ...
Ø High
error-susceptibility.
PROPOSED SYSTEM
In
today’s society, medical care problems have become a hot topic, and problems
such as the unbalance and insufficient allocation of medical resources has
become increasingly apparent. In this situation, the application of ML has
become the unavoidable trend in the current development of medical care. As
early as 1972, the scientists in the University of Leeds in the UK has been
trying to use artificial intelligence (ANN) algorithms to judge abdominal pain.
Now, more and more researchers are committed to the combination of ML and
medical care. The methods of pathological diagnosis of tumors, lung cancer,
etc. by ML has gradually entered the field of vision. Some companies, such as
Alibaba, Amazon, and Baidu have established their own research team working for
it. This introduction of ML in medical care has greatly saved medical resources
and provided a new way for citizens to see a doctor and facilitate people’s
lives. At the same time, the demand of people also provides a new impetus for
the research and development of ML, with promoting its continuous improvement.
ADVANTEGES:
Ø
Identifying Diseases and Diagnosis. ...
Ø
Drug Discovery and Manufacturing. ...
Ø
Medical Imaging Diagnosis. ...
Ø
Personalized Medicine.
...
Ø
Machine Learning-based Behavioral
Modification. ...
Ø
Smart Health Records.
...
Ø
Clinical Trial and Research. ...
Ø
Crowdsourced Data Collection.
SYSTEM
ARCHITECTURE
SYSTEM SPECIFICATION:
HARDWARE REQUIREMENTS:
v
System : Pentium IV 2.4 GHz.
v
Hard Disk :40 GB.
v
Floppy Drive : 1.44 Mb.
v
Monitor : 14’ Colour
Monitor.
v
Mouse : Optical Mouse.
v Ram
:512 Mb.
SOFTWARE REQUIREMENTS:
v Operating system : Windows
7 Ultimate.
v Coding Language : Python.
v Front-End :
Python.
v Designing :Html,css,javascript.
v Data Base :
MySQL.
REFERENCES
[1] G. Eason, B. Noble, and I.N. Sneddon, “On
certain integrals ofLipschitz-Hankel type involving products of Bessel
functions,” Phil.Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955.(references)
[2] Jiang M, Zhang S, Huang J, et al.
Scalable histopathological imageanalysis via supervised hashing with multiple
features[J]. MedicalImage Analysis, 2016, 34:3-12.
[3] Joanna J K, Pawel K . Automatic
Classification of SpecificMelanocytic Lesions Using Artificial Intelligence[J].
BioMedResearch International, 2016, 2016:1-17.
[4] Lu-Cheng, Zhu,Yun-Liang, Ye,Wen-Hua,
Luo,Meng, Su,Hang-Ping,Wei,Xue-Bang, Zhang,Juan, Wei,Chang-Lin, Zou.A model
todiscriminate malignant from benign thyroid nodules using artificialneural
network.[J].PloS one,2013,8(12):e82211.
[5] Huang W C , Chang C P . Automatic Nasal Tumor
Detection by greyprediction and Fuzzy C-Means clustering[C]// IEEE
InternationalConference on Systems. IEEE, 2006.M. Young, The TechnicalWriter’s
Handbook. Mill Valley, CA: University Science, 1989.
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