Machine Learning Is The Future Of Cancer Prediction

Source: Deep Learning on Medium

Machines have gotten pretty good at predicting the development of cancer — and they’re getting better.

Photo by Ken Treloar on Unsplash

Every year, Pathologists diagnose 14 million new patients with cancer. That’s millions of people who’ll face years of uncertainty.

Pathologists have been performing cancer diagnoses and prognoses for decades. Most pathologists have a 96–98% success rate for diagnosing cancer. They’re pretty good at that part.

The problem comes in the next part. According to the Oslo University Hospital, the accuracy of prognoses is only 60% for pathologists. A prognosis is the part of a biopsy that comes after diagnosing cancer. It’s basically predicting the development of the disease.

It’s time for pathology to take its next step.

Introducing Machine Learning

The next step in pathology is Machine Learning.

Machine Learning (ML) is one of the core branches of Artificial Intelligence. It’s a system which takes in data, finds patterns, trains itself using the data and outputs an outcome.

So what makes a machine better than a trained professional?

ML has key advantages over Pathologists.

Firstly, machines can work much faster than humans. A biopsy usually takes a Pathologist 10 days. A computer can do thousands of biopsies in a matter of seconds.

Machines can do something which humans aren’t that good at. They can repeat themselves thousands of times without getting exhausted. After every iteration, the machine repeats the process to do it better. Humans do it too, we call it practice. While practice may make perfect, no amount of practice can put a human even close to the computational speed of a computer.

Another advantage is the great accuracy of machines. With the advent of the Internet of Things technology, there is so much data out in the world that humans can’t possibly go through it all. That’s where machines help us. They can do work faster than us and make accurate computations and find patterns in data. That’s why they’re called computers.

Brief Technical Explanation of Machine Learning

To begin, there are two broad categories of Machine Learning,

  1. Supervised Learning
  2. Unsupervised Learning

Supervised Learning is Fed Labeled Data

Supervised learning is perhaps best described by its own name. A supervised learning algorithm is an algorithm which is “taught” by the data it is given.

The model trains itself using labeled data and then tests itself. This is repeated until the optimal result is achieved. Once this is done, it can make predictions on future instances.

Unsupervised Learning Draws Conclusions from Unlabeled Data

In unsupervised learning data sets are not labeled. Instead, it’s the model’s job to create a structure that fits the data by finding patterns (such as groupings and clustering).

Think of unsupervised learning as a baby. Babies are born into this world without any knowledge of what’s “right” or “wrong” other than instincts. As they grow, they see, touch, hear and feel(input data) and try things out (test on the data) until they’ve learned about what it is.

Alright, you know the two main categories of ML. Cool. Now let’s dive a bit deeper into some of the techniques ML uses.

Regression Makes the Outcome More Accurate

Regression’s main goal is to minimize the cost function of the model.

What’s cost function?