Machine Learning has been a major part of the healthcare industry. It is changing the way patient care is done, and also works in favor of the overall industry operations.
One of the most effective areas of ML application has been in diagnosis. As a machine is taught how to read the reports, it can result in basic results. Moreover, other than basic results there are various types of cancers and other genetic diseases that usually are hard to detect. But, with the support of ML and applications, these can be located at the initial stages. Some of the examples are:
- IBM Watson Genomics Project- It combines cognitive computing and genome-based tumor sequencing. This helps in quick diagnosis and prediction of the disease.
- Predicting Response To Depression Treatment- PReDict is trying to bring AI and ML into play to diagnose and treat issues like depression practically at usual hospitals too.
Real-World Machine Learning Implementation For Prediction
Machine learning is impacting the way prediction for any disease is done. Through technological up-gradation, many diseases can be detected and cured. Two such instances are
- Diabetes Prediction
One of the common yet dangerous diseases is diabetes. It also leads to other illnesses. Diabetes affects the heart, lungs, kidneys, and nerves. Early diagnosis of this disease can result in reverse and cure. There are some algorithms like KNN and Naive Bayes that can be used as the basis for diabetes detection.
- Liver Disease Prediction
The liver has a major role in metabolism management. The organ can catch diseases like hepatitis, cirrhosis, etc. It is difficult to predict diseases related to the liver, but with correct data storage and analysis via ML, a shift can be found. There are ML algorithms like clustering, classification, etc. that can create such a shift.
Other than these two, there are many diseases that can be predicted at an early stage via machine learning. It’s time we utilize the power and put efforts toward the healthcare future.