Why the research work on the applicability of machine learning algorithms for classifying medical data is huge and in trend. What qualities do machine learning algorithms posses, that they are being chosen for implementation.
I am new to this field but I will try my best to answer the question.
Machine Learning algorithms have some very interesting capabilities which makes them suitable for said applications. Some of which, I think important in case of medical applications are as following:
1. Flexible: Machine learning algorithms can be applied to both linear and non-linear data-sets. In medical field most of the data collected is non-linear in nature and hence machine learning is efficient for these applications.
2. Noise immunity: Machine learning algorithms are immune to noise present in data. Medical data is prone to have noise and hence again use of machine learning algorithms is beneficial.
3. Data required: In medical field it is not possible to collect data on regular bases if data is collected from patients and some readings may be missing. Machine learning algorithms can deal with those missing data without creating any error in output. It is also related to cost of data acquisition. Acquiring data can be costly and in those cases, collected data may not be sufficient. Machine learning algorithms applied correctly, can give near to accurate answer even when data is not sufficient.
Other reasons include:
- Fast and efficient.
- Easy implementation.
- Availability of computational resources.
- Badges
- Science topic