Machine required when predicting cancer because even one

Machine
learning, the ability of machines to learn
without being explicitly programmed, has proved to be a promising part of
artificial intelligence. Because of new computing technologies, machine
learning today is not like machine learning of the past. It was born from
pattern recognition and the theory that computers can learn without being
programmed to perform specific tasks; researchers interested in artificial
intelligence wanted to see if computers could learn from data. The
iterative aspect of machine learning is important because as models are exposed
to new data, they are able to independently adapt. They learn from previous
computations to produce reliable, repeatable decisions and results. It’s a
science that’s not new – but one that has gained fresh momentum.

Artificial
intelligence has been the root to solving problems in many fields like
Economics, Robotics, Linguistics, Medical diagnosis and many others Machine learning in cancer research dates back
to the 20th century. Machine learning can be supervised, unsupervised
and semi-supervised, the later proving to
be more useful. New methods consisting of modified algorithms are being
implemented in predicting, treating cancer. Efficiency is highly required when
predicting cancer because even one life matters. If our method can provide us
with accuracy of 100% then only it means
not even one prediction was wrong. So, search for the perfect method of
predicting cancer is essential. 

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ANN
(artificial neural network) has been the gold standard, BN(Bayesin network) proved to be good in
predicting certain cancers like colon cancer, 
Decision Trees (DT) has been efficient too and  deep learning is that promising method that
has been recently researched about and something that has not been yet included
in that many review papers. It uses a variety of optimization techniques that permits us to learn from past training and
detect complex patterns from large and complex data sets. In cancer prediction
we need a large dataset for training and testing, deep learning could be more
efficient and in comparison to other methods
it could be the best.

Cancer
is the general name for a group of more than 100 diseases. Although cancer
includes different types of diseases, they all start because abnormal cells
grow out of control. Without treatment, cancer can cause serious health
problems and even loss of life. Early detection of cancer may reduce mortality
and morbidity. AI techniques are approaches that are utilized to produce and
develop computer software programs. AI is an application that can re-create
human perception. This application normally requires obtaining input to endow
AI with analysis or dilemma solving, as well as the ability to categorize and
identify objects. This paper describes various AI techniques, such as support
vector machine (SVM) neural network, fuzzy models, artificial neural network
(ANN), and K-nearest neighbor (K-NN). Feedforward neural networks that are
capable of classifying cancer cases with high accuracy rate have become an
effective tool. Computation time is fixed, and extremely high computation speed
results from the parallel structure. Moreover, the approach is fault-tolerant
because of the distributed nature of network knowledge. General solutions can
be learned from presented training data. Neural networks eliminate the
requirement to produce an explicit model of a process. Moreover, these networks
can easily model parts of a process that cannot be modeled or even usually
unidentified. A neural network could learn from incomplete and noisy data.