Machine learning through its applications has steadily made us aware of its extreme potential in multiple fields including the medical industry. We come across multiple instances where a lot of big companies have used machine learning to create medical imaging data through which radiologists and physicians can complete cancer diagnoses with extreme precision.
Furthermore, while using complex algorithms the verisimilitude of such clinical diagnoses has become ameliorated. The application of machine learning to analyze medical data is undoubtedly a tremendous feat. Yet, interpreting these avant-garde algorithms (also known as black-box) is not an easy task.
The deep neural network is a prominent example of a black-box model. In deep neural networks, any individual decision is taken after the input data has gone through multiple neurons available in the network.
Physicians cannot properly diagnose with the help of these kinds of black-box models because these black-box models do not include the experiences and prior knowledge of the physician. Therefore, physicians cannot believe in such model-based diagnoses.
To support this statement we can look at a recent survey conducted on European patients with the help of black-box models. According to this survey, around 55.4% of physicians claim that their patients are not ready to accept the outcome of the purely artificial intelligence-based diagnosis if they do not administer the diagnosis themselves.
About explainable artificial intelligence
With the expansion of complexity in black-box models, the precision of the model increases but at the same time, the explicability of the model also decreases. Most of the machine learning models are hard to explain because of the involvement of complex mathematics and that is why some predictions made by black-box are hard to understand.
With the help of explainable artificial intelligence methods, you will be able to decipher and fathom the prognosis of the machine learning models. Using the explainable AI frameworks companies can come across more useful solutions.
The word LIME is the acronym for Local Interpretable Model-agnostic Explanations. It was presented in 2016 by Carlos Guesterin, Marco Tulio Ribeiro and Sameer Singh. LIME is a visualization technique that can help in understanding individual outcomes from black-box models.
LIME works based on an assumption. It assumes that every complex model is linear on a local scale. In this technique, a simple model is fit into a single observation so that the global model will also follow it and behave accordingly. Then this simple model will be used to describe the outcome or predictions of the other black-box models.
LIME algorithm: A step-by-step process
When the LIME algorithm is used on a high level, it follows the process mentioned below:
- Make a discombobulated data
- Anticipate the result of the discombobulated data
- Create disconnected features
- Discover the euclidean distance
- Transform the distance to the similarity score
- Choose the best features for the model
- Explain the prediction using a linear model
LIME is an extremely versatile algorithm because you can use it in a lot of different cases apart from images and texts. Although there is still room for improvement, LIME can be utilized to invigorate the contumacy and functionality of the black-box models.