Machine learning helps in cancer detection and prognosis in several ways. It can analyze large datasets to identify patterns related to cancer susceptibility, recurrence, and survivability. Supervised and unsupervised machine learning have been used to extract meaningful features from gene expression and medical imaging data to classify cancers and segment tumors. This reduces errors, improves accuracy, and eliminates manual, time-consuming analysis compared to methods relying solely on human expertise. Machine learning is advancing medical research and cancer treatment by allowing vast amounts of data to be processed efficiently.

1. Data Science
Session 1
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How Does Machine Learning Help in Cancer
Detection?
Oncology is one such field of medical science that is constantly evolving. The
word cancer still evokes fear and shock in everyone’s mind. Cancer is a
heterogeneous disease which consists of many different subtypes. Early diagnosis
and prognosis of this deadly disease have become a necessity in cancer research.
The advent of new technologies in the field of medical sciences have helped the
medical research community to analyze a vast amount of data sets. The only
hindrance is to access the accurate prediction of the disease, which is interesting
and challenging.
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Adaption of Machine learning techniques have now become a powerful tool to
discover and identify patterns between complex data sets and effectively predict
the outcomes.
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Machine Learning Techniques
Machine learning is a branch of Artificial Intelligence which learns from data
samples and use that to classify new data, identify new patterns or predict trends.
Machine learning has proven to be a boon for biomedical research to help the
researchers search through an n-dimensional space for a given set of samples
using different algorithms and techniques.
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Mainly, there are 2 common types of machine learning methods, supervised
learning and unsupervised learning. In supervised learning, data is labeled. The
model identifies the labels and groups accordingly. Simply put, the model is
provided with all the inputs and then told the expected output. In contrast to this,
unsupervised learning methods have no label for data.
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The models identify different features and classify them based on different
characteristics. In this method, the input is provided and the computer then learns
to find patterns and make logical classification or groupings.
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Application To Cancer Research
The elemental goals of cancer prognosis and prediction are different from the
goals of cancer diagnosis and detection. In cancer prediction and prognosis there
are three center points:
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1. Prediction of cancer susceptibility ( risk assessment)
2. Prediction of cancer recurrence
3. Prediction of cancer survivability
Risk assessment is more about trying to predict the likelihood of developing some
type of cancer before the occurrence of the disease. In the prediction of cancer
recurrence, one tries to predict the likelihood of redevelopment of cancer, right
after the apparent resolution of the disease.
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The last point, prediction of cancer survivability predicts the outcome after the
disease has been diagnosed, such as survivability, life expectancy, progression,
tumor-drug sensitivity.
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Cancer Detection From Data Set
Gene expression data is very complex as it is highly dimensional and makes it
challenging to leverage that data in cancer detection. Researchers have been able
to use deep learning to extract meaningful features from the gene expression data
which has enabled the classification of breast cancer cells. In the past, Google’s
CNN system has showcased the ability to identify deadline skin cancers.
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Researchers from China have leveraged deep learning for segmenting brain
tumors in Magnetic Resonance (MR) imaging which yielded more stable results in
comparison to one done manually by physicians as it was more prone to vision
errors. Machine learning has also assisted in measuring the size of tumors
undergoing treatment and detect other metastases which might have been
overlooked.
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The biggest benefit of using Artificial intelligence and machine learning is that
these next-gen technologies have the capability to read a vast amount of data with
utmost accuracy, thus eliminating the manual time-consuming process. The
technologies also help to reduce dependence on the limited judgment and skills of
a specialized expert. Machine learning is paving the way for the future in medical
sciences since research on cancer, its cure and treatment has been a prominent
focus for ages.
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