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Various ways at which Machine Learning could be used in Medical Diagnosis

Machine Learning is being used in many industries such as automobile, manufacturing, and retail industries. With the development of machine learning and deep learning algorithms, computation is becoming easy. Furthermore, there is data available in different formats that could be used for machine learning predictions. As the data keeps growing, there is a lot of scope for development in the field of machine learning and predictions are going to get better and better in the future.

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One of the interesting applications of machine learning is in the field of healthcare. We’ve seen a few movies on the internet where they show robots performing the work of a doctor and making the right kind of diagnosis respectively. Movies such as Ender’s game show how robots are used in medical diagnosis. There are many new applications for machine learning created in the field of medical diagnosis. As a result, there is a lot of scope and improvement in the field.

There are more and more sophisticated algorithms being developed with the aid of machine learning and data science. Some of the cool applications of machine learning in healthcare are to predict the chances of occurrence of cancer and predicting Alzheimer’s disease. Taking a look at these applications, we can come to a conclusion that machine learning is still growing and there would be an increase in the demand for it in the future as well. There is still a lot of demand for data science and machine learning. Now, there are more and more sophisticated algorithms being developed that are being used in machine learning to make robust predictions.

There are machine learning models implemented in Radiology where the machines are making predictions which would ensure that we are getting the best results on the test set as well.

Challenges of using Machine Learning in Healthcare

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Though there is a lot of potential for using machine learning and data science in healthcare, there are some challenges when performing machine learning analysis.

There could be challenges when trying to implement machine learning models for healthcare data. One of the challenges when dealing with healthcare data is that the data might be causal for machine learning models. What is meant by causality is that when there is data and if one feature causes the occurrence of the other, the relationship could be said to be having a high causality. In machine learning for most of the algorithms, we assume that the features are independent of each other without one feature causing the other to occur and vice-versa. Therefore, this assumption would be weakened when there is high causality between features respectively.

Shortage of Data Scientists and Machine Learning Engineers

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So far, we’ve talked about data and machine learning algorithms that were the limitations of using data science in Healthcare. However, if there is a shortage of people would be using these algorithms, then the implementation of artificial intelligence in the medical industry would be a major challenge as well. Since the number of machine learning and data science courses in institutions has increased, there is a higher chance of talented professionals entering the field and making it a success.

Bias would be present in machine learning

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When performing the machine learning tasks, there would be a presence of bias which could lead to the machine learning models not performing well on the unseen or the test set respectively. The bias that is present in machine learning models would be due to the type of data that is fed to machine learning models. Consider, for example, if the data that is given to the models contain a lot of information about a particular class and less information about the minority class without taking into account different scenarios, the machine learning models would make predictions on the test set that would be used in the long term that is going to make the predictions of the use of machine learning and data science.

Lack of Quality Data

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Though there is a huge possibility of machine learning algorithms to be put to use, there is still a lot of data that is required in the field of medicine to make the most use of it. The data that is present in the form of medical images is quite low in number to be significantly used to test. Furthermore, data that is present is not labeled so that it could be used for machine learning purposes. It really takes a long time to label large volumes of data for machine learning.

Data annotation must be done accurately

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Data is present everywhere in the form of medical images and other useful information. Though there is this huge presence of data, annotated examples or the output label for predictions are not present. Since some of the best machine learning algorithms would be working with supervised, we need to be providing the data that is annotated. This would ensure that the annotated examples would help the machine learning models to be fitting and ensuring that there are predictions that are accurate respectively. In medical data, there is a requirement to annotate the data which is a time-consuming process. Therefore, this is one of the challenges for using machine learning in medicine respectively.