Top Applications of Machine Learning in Healthcare

The applications of machine learning in healthcare include detection and diagnosis of disease, drug discovery,  and personalized medicine. Nicholas Walker describes how ML is being used to advance healthcare and medical research. 

The number of patients in hospitals is growing rapidly, which means it’s getting more and more challenging to analyze, and even record, all the data on patients today. A good solution to this problem is machine learning, which makes it easier to automate the analysis of data and makes the healthcare system more robust. Machine learning, as applied to healthcare, is the confluence of two fields: medical science and computer science. This alliance has allowed the medical field to make huge advances in healthcare. 

There is plenty of research being carried out in this area. Google, for example, has invented an algorithm that detects cancer cells. There are many other advances being made as well, which we shall talk about in this article.

Healthcare Applications of Machine Learning

The purpose of machine learning in computer science is to make the machine more efficient and reliable. In healthcare, the machine is an extension and force multiplier for the doctor’s brain. After all, a patient will always need the touch and care of a human being, which a machine cannot provide. The work of a machine, therefore, is not to replace the doctor, but rather to help him or her to provide better service and care. 

1. The Diagnosis of Heart Disease

The heart is one of the primary organs in our bodies. There are a variety of heart ailments that we suffer from, such as coronary heart disease, coronary artery disease, and so on. Researchers are in the process of developing machine learning algorithms to make it easier to diagnose heart disease. It is a highly researched topic globally and an automated system for the diagnosis of heart disease would be one of the greatest feats of human achievement in the 21st century. 

Researchers are working on Support Vector Machines, Naïve Bayes, and other forms of supervised machine learning algorithms to solve the problem of heart disease detection and diagnosis. One of the most important datasets in this field is the one from UCI, Which can be used to train algorithms. 

2. The Prediction of Diabetes

Diabetes is not only a dangerous disease, but it also happens to be one of the most common diseases in the world. It is also a gateway disease, being itself one of the leading causes of other diseases and leading its victims inexorably towards death. 

Diabetes has the ability to damage various parts of the body, such as the heart, kidney, and the nervous system. Machine learning is being looked into as a way of detecting the markers of diabetes early enough so the lives of patients can be saved. 

There are many algorithms that can be used to predict diabetes, including Naïve Bayes, Decision Trees, Random Forests, and KNNs. The Naïve Bayes outperforms the others when it comes to accuracy because of how good its performance is and how little time it takes to do computation. 

3. The Prediction of Liver Disease

The liver is yet another organ that is among the primary organs in the body. It is crucial for metabolism and can be attacked by a host of diseases, including liver cancer, chronic hepatitis, liver cirrhosis, and many others. 

Data mining and machine learning concepts have recently come into play in the quest for a system to predict liver disease. To be honest, it is quite a challenging endeavor to try and predict liver disease, partly because there are so many possible diseases that could attack the liver and also partly because there is such a huge volume of data on the subject. 

Researchers, however, are doing the best they can to work around these issues. A lot has been written by various essay writing services in the united states about the use of machine learning techniques like clustering, classification, and so on. There are also available datasets researchers are using to develop their algorithms. 

4. ML Applications in Surgery

Surgery, in particular robotic surgery, is one of the most promising applications of machine learning in healthcare. It isn’t just one big field but an umbrella category with about 4 sub-fields: surgical skill evaluation, automatic suturing, surgical workflow modeling, and improvement of robotic surgical materials.

Suturing is the process of sewing up a wound. When it’s automatic, it makes the surgical procedure take a lot less time and relieves stress from the surgeon. Researchers are putting in a lot of work in this field, applying the principles of machine learning to the different aspects of surgery and working toward a future where robot-assisted surgery will be both effective and safe, and perhaps even minimally invasive. 

In neurosurgery, for example, robots are not yet as effective as neurosurgeons would like them to be. As a result, virtually all of the procedures are manual and the whole process is quite time consuming. There also isn’t any automatic feedback. The development of machine learning in this field will prove greatly beneficial.

5. The Detection of Cancer

Machine learning and its different approaches are being used extensively to predict and detect various types of tumours. Deep learning is also very important in this field since there is no shortage of data and the method is accessible. In fact, deep learning has been quite successful in the diagnosis of breast cancer and has greatly increased accuracy in that field. 

DeepGene, a deep learning classifier for cancer types, has been extensively explored by Chinese researchers. One of the most promising ways to predict cancer that machine and deep learning are being applied to is the extraction of features from data on gene expression. This approach lends itself especially well to convoluted neural networks, a type of machine learning algorithm.

6. The Discovery of New Drugs

Machine learning is being extensively used in the discovery of drugs and it is proving to quite promising. Microsoft has the Project Hanover, which is looking to improve precision medicine using machine learning techniques. There are several other companies working on the same project, all of them using different promising approaches to the problem. 

Machine learning presents several benefits when applied to the science of healthcare. It will make the process of discovering new drugs faster and also less error prone by drastically bringing down the failure rate. It will also reduce the cost of drug discovery by optimizing the drug manufacturing process.

7. The Personalization of Treatment

Machine learning as applied to the personalization of treatment is one of the most hotly researched areas in both healthcare and machine learning. The goal of personalized treatment is to be able to improve individual health services by using highly individual data and analytical techniques. Machine learning tools for computation and statistics are used in this area to develop personalized treatment systems based on the genetic information and symptoms of the patient. 

Supervised machine learning algorithms are used in the development of personalized treatment systems using individual medical information from patients. 

Conclusion

The applications of machine learning in healthcare is helping to develop and deliver personalized medicine, improve the quality of life and detect disease early on. The future is both promising and bright. Machine learning promises to advance healthcare to extents that we might not be able to imagine today. In the future, the power of computers might be brought to bear on the physical ailments of humanity, making us truly immortal beings. 

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