With the rapid growth of the population, it seems challenging to record and analyze the massive amount of information about patients. Machine learning provides us such a way to find out and process this data automatically which makes the healthcare system more dynamic and robust. Machine learning in healthcare brings two types of domains: computer science and medical science in a single thread. Machine learning technique brings an advancement of medical science and also analyze complex medical data for further analysis.
Several researchers are working in this domain to bring new dimension and features. Recently, Google has invented a machine learning algorithm to detect cancerous tumors on mammograms. Additionally, Stanford presents a deep learning algorithm to determine skin cancer. Every year, several conferences, e.g., Machine Learning for Healthcare, are being held to pursue new automated technology in medical science to provide better service.
Applications of Machine Learning in Healthcare
The purpose of machine learning is to make the machine more prosperous, efficient, and reliable than before. However, in a healthcare system, the machine learning tool is the doctor’s brain and knowledge.
Because a patient always needs a human touch and care. Neither machine learning nor any other technology can replace this. An automated machine can provide the service better way. Below, the top 10 applications of machine learning in healthcare are described.
1. Heart Disease Diagnosis
The heart is one of the principal organs of our body. We often suffer a variety of heart diseases like Coronary Artery Disease (CAD), Coronary Heart Disease (CHD), and so forth. Many researchers are working on machine learning algorithms for heart disease diagnosis. It is a very hot research issue all over the world. An automated heart disease diagnosis system is one of the most remarkable benefits of machine learning in healthcare.
Researchers are working several supervised machine learning algorithms like Support Vector Machine (SVM) or Naive Bayes to use as a learning algorithm for heart disease detection.
The Heart disease dataset from UCI can be used as a training or testing dataset or both. The WEKA data mining tool can be used for data analysis. Alternatively, if you want, you can use an Artificial Neural Network (ANN) approach to develop the heart disease diagnosis system.
2. Predicting Diabetes
Diabetes is one of the common and dangerous diseases. Also, this disease is one of the leading causes to create any other severe illness and towards death. This disease can damage our various body parts like kidney, heart, and nerves. The objective of using a machine learning approach in this field is to detect diabetes at an early stage and save patients.
As a classification algorithm, Random forest, KNN, Decision Tree, or Naive Bayes can be used to develop the diabetes prediction system. Among these, Naive Bayes outperforms the other algorithms in terms of accuracy. Because its performance is excellent and takes less computation time. You can download the diabetes dataset from here. It contains 768 data points with nine features each.
3. Prediction of Liver Disease
The liver is the second most significant internal organ in our body. It plays a vital role in metabolism. One can attack several liver diseases like Cirrhosis, Chronic Hepatitis, Liver Cancer, and so on.
Recently, machine learning and data mining concepts have been used dramatically to predict liver disease. It is very much challenging task to predict disease using voluminous medical data. However, researchers are trying their best to overcome such issues using machine learning concepts like classification, clustering, and many more.
Indian Liver Patient Dataset(ILPD) can be used for a liver disease prediction system. This dataset contains ten variables. Or, liver Disorders Dataset can also be used. As a classifier, Support Vector Machine (SVM) can be used. You can use MATLAB to develop the liver disease prediction system.
4. Robotic Surgery
Robotic surgery is one of the benchmark machine learning applications in healthcare. This application will become a promising area soon. This application can be divided into four subcategories such as automatic suturing, surgical skill evaluation, improvement of robotic surgical materials, and surgical workflow modeling.
Suturing is the process of sewing up an open wound. Automation of suturing may reduce the surgical procedure length and surgeon fatigue. As an instance, The Raven Surgical Robot. Researchers are trying to apply a machine learning approach to evaluate surgeon performance in robot-assisted minimally invasive surgery.
The University of California, San Diego (UCSD) Advanced Robotics and Controls Lab researchers are trying to explore machine learning applications to improve surgical robotics.
As, in the case of neurosurgery, robots are not able to operate effectively. The manual surgical workflow is time-consuming, and it can not provide automatic feedback. Using a machine learning approach, it can speed up the system.
5. Cancer Detection and Prediction
At present, machine learning approaches are being used to detect and classify tumors extensively. Also, deep learning plays a significant role in cancer detection. As deep learning is accessible and data sources are available. A study showed that deep learning reduces the percentage of error for breast cancer diagnosis.
Machine learning has proven its capabilities to detect cancer successfully. China researchers explored DeepGene: a cancer type classifier using deep learning and somatic point mutations. Using a deep learning approach, cancer can also be detected by extracting features from gene expression data. Moreover, the Convolution Neural Network (CNN) is being applied in cancer classification.
6. Personalized Treatment
Machine learning for personalized treatment is a hot research issue. The goal of this area is to provide better service based on individual health data with predictive analysis. Machine learning computational and statistical tools are used to develop a personalized treatment system based on patients’ symptoms and genetic information.
To develop the personalized treatment system, a supervised machine learning algorithm is used. This system is developed using patient medical information. SkinVision app is the example of personalized treatment. By using this app, one can check his/her skin for skin cancer on his/her phone. The personalized treatment system can reduce the cost of healthcare.
7. Drug Discovery
The use of machine learning in drug discovery is a benchmark application of machine learning in medicine. Microsoft Project Hanover is working to bring machine learning technologies in precision medicine. At present, several companies are applying machine learning technique in drug discovery. As an instance, BenevolentAI. Their objective is to use Artificial Intelligence (AI) in drug discovery.
There are several benefits of applying machine learning in this field, such as it will speed up the process and reduce the failure rate. Also, machine learning optimizes the manufacturing process and cost of drug discovery.
8. Smart Electronic Health Recorder
Machine learning scope such as document classification and optical character recognition can be used to develop a smart electronic health record system. The task of this application is to develop a system which can sort patient queries via email or transform a manual record system into an automated system. This objective of this application is to build a safe and easily accessible system.
The rapid growth of electronic health records has enriched the store of medical data about patients, which can be used for improving healthcare. It reduces data errors, for example, duplicate data.
To develop the electronic health recorder system supervised machine learning algorithm like Support Vector Machine (SVM) can be used as a classifier or Artificial Neural Network (ANN) can also be applied.
9. Machine Learning in Radiology
Recently, researchers have been working to integrate machine learning and artificial intelligence in radiology. Aidoc provides software for the radiologist to speed up the process of detection using machine learning approaches.
Their task is to analyze the medical image to offer the intelligible solution for detecting abnormalities across the body. The Supervised machine learning algorithm is used mostly in this field.
For medical image segmentation, machine learning technique is used. Segmentation is the process of identifying structures in an image. For image segmentation, the graph cut segmentation method is used mostly. Natural Language Processing is used for analysis for radiology text reports. Therefore, applying machine learning in radiology can improve the service of patient care.
10. Clinical Trial and Research
The clinical trial may be a set of queries that require answers to obtain the efficiency and safety of an individual biomedical or pharmaceutical. The purpose of this trial is to focus on the new development of treatments.
This clinical trial costs a lot of money and time. Applying machine learning in this field has a significant impact. An ML-based system can provide real-time monitoring and robust service.
The benefit of applying machine learning technique in clinical trial and research is that it can be monitored remotely. Also, machine learning provides a safe clinical environment for patients. Using supervised machine learning in healthcare can enhance the efficiency of the clinical trial.
Nowadays, machine learning is part and parcel of our everyday life. This technique is used in a variety of domains such as weather forecasting, marketing applications, sales prediction, and many more. However, machine learning in healthcare is still not so wide-ranging like other machine learning applications because of having the medical complexity and scarcity of data. We firmly believe this article helps to enrich your machine learning skill.
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Thanks for sharing useful information.Machine Learning is very advance technology and it is helping in almost every field. for healthcare industry also, machine learning is helping from clinical research to keeping records of patient’s health.