Top 10 Potential Applications of ML in Healthcare

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Artificial Intelligence (AI) and Machine Learning (ML) are having promising growth in the field of Data Science. Their applications have spread into every possible domain and are yielding successful outcomes. Among those benefitted domains, Healthcare takes a prominent role as it is the most essential form to analyze the Standard of living of a nation.

There is a rapid growth in population and so are the diseases. Hence, it seems challenging to record and analyze such a massive amount of information about the patients. Here comes into the picture, Machine Learning, a major element of Data Science. Machine Learning provides us a way to find out and automatically process this massive data making the healthcare system more dynamic and robust. ML in healthcare brings two domains computer science and medical science in a single thread. This ML technique gains the advancement of medical science and analyzes complex medical data.

Many researchers are working on this domain to bring out new dimensions and features. Every year, several conferences will be conducted to pursuing new automated technology in medical science to provide better service.
The purpose of Machine Learning is to enable machines to work efficient and reliable to yield prosperous results than ever before. However, a machine learning tool is a doctor’s brain and knowledge in a healthcare system. Constant monitoring or health backup is essential to every individual. It is not every time the reports are flexible to carry and make a diagnosis for every visit to the doctor. So, in such cases, an automated machine provides the service in a better way.

Here are the top 10 potential applications of Machine Learning that showed profound interest:

1. Diagnosis of heart disease

Heart, one of the principal organs of the body suffers from a variety of diseases like Coronary Heart Disease (CHD), Coronary Artery Disease (CAD), etc. Researchers are working on machine learning algorithms for diagnosing heart diseases which is a very hot research issue in the world.

An automated heart disease diagnosis system stands as one of the most remarkable benefits of machine learning in healthcare. Research work is happening on several supervised ML algorithms like Support Vector Algorithm (SVM) that can be used for heart disease detection. The Heart disease dataset from UCI can be used as a testing and training dataset. There is a tool called ‘WEKA data mining’ which can be used for data analysis. Also, an Artificial Neural Network (ANN) approach can be deployed to develop the heart disease diagnosis system.

2. Diabetes Prediction

Nowadays, diabetes is one of the most common and dangerous diseases. This disease is also one of the leading causes to cause several other illnesses and even leads to death. This disease can even damage various body parts like heart, kidney, and nerves.

Detecting the disease at an early stage is the main objective of using a machine learning approach in this field.

For classification, algorithms like Random Forest, Decision Tree, KNN, or Naïve Bayes can be used to develop a diabetes predictive system. Among these algorithms, Naïve Bayes outperforms other algorithms in terms of performance and accuracy as it takes less computation time.

3. Prediction of Liver Disease

The second most significant organ of our body is the liver. It plays an important role in metabolism. Several common ailments to the liver include Chronic hepatitis, Cirrhosis, Liver Cancer, etc. Machine Learning and data mining concepts have been used to predict liver disease. It is a challenging task to predict the disease using voluminous medical data. But researchers are trying to overcome such issues using ML concepts like classification, clustering, etc.

Indian Liver Patient Dataset (ILPD) or Liver disorders Dataset comes handy to use for making a liver prediction system. Support Vector Machine (SVM) can be used as a classifier. MATLAB can also be used to develop the liver disease prediction system.

4. Detection and Prediction of Cancer

In recent days, machine learning approaches are useful to detect and classify tumors extensively. Deep Learning also plays a significant role in cancer detection. A study suggested that deep learning has less percentage of error in breast cancer diagnosis.

ML has proven capabilities in detecting cancer successfully. ‘Deep Gene’ is a cancer type classifier that used deep learning and somatic point mutations. By extracting features from gene expression data, cancer detection is done using deep learning. In the classification of cancer, Convolution Neural Network (CNN) finds the best use.

5. Robotic Surgery

One of the benchmark machine learning applications in healthcare is Robotic Surgery. This application becomes a promising area in the future. It can be divided into four categories like automatic suturing, improvement of robotic surgical methods, surgical skill evaluation, and surgical workflow modeling. Suturing is a process of sewing up the open wounds. Automation of suturing reduces the length of surgical procedure and surgeon fatigue. Researchers are trying to apply an ML approach to evaluate surgeon performance in robot-assisted surgery.

6. Personalized Treatment

Better service based on individual health data with predictive analysis is the goal of personalized treatment. Based on patients’ symptoms and genetic information, ML statistical and computational tools are used to develop a personalized treatment system. A supervised ML algorithm is used to develop the personalized treatment system by using patients’ medical data.

7. Drug Discovery

The use of ML in drug discovery is the most seeking application in medicine. Many companies are applying the ML technique in drug discovery. For example ‘Benevolent AI’ is using Artificial Intelligence for discovering drugs. There are numerous benefits of applying ML in this field as it reduces the failure rate and speeds up the process. ML also optimizes the cost of drug discovery and the manufacturing process.

8. ML in Radiology

Researchers have been working to integrate AI and ML in radiology. Software by ‘Adioc’ provides a quick process of detection using ML approaches. The task is to analyze a medical image to offer an intelligent solution in detecting the abnormalities in the body. The Supervised ML is the most used one in this field.

ML technique is used for medical image segmentation to identify the structures in an image. Graph cut segmentation process is used for image segmentation and Natural Language Processing is used for analysis for radiology text reports. So, ML application in radiology can improve the service of patient care.

9. Smart Electronic Health Recorder

Document classification and optical character recognition have a scope in ML to develop a smart electronic health recorder. The task of this application is to develop a system that sorts patients’ queries through email or convert a manual record system into an automated system. There is a rapid growth of electronic health records that necessitated the store of medical data about patients. It can be used to improve healthcare and reduce data errors.

Supervised ML algorithms like SVM can be used as classifiers or ANN can also be used to develop the electronic health recorder.

10. Clinical Research and Trials

Clinical trial costs a lot of money. A clinical trial may be a set of queries that requires the efficiency and safety of an individual. Applying ML has a significant impact in this field. Real-time monitoring and robust service can be provided by an ML-based system.
One of the major benefits of ML in clinical research and trials is that the process can be remotely monitored. A supervised ML in clinical trials provides a safe environment for patients and enhance the efficiency of clinical trials.

In the current world, Machine Learning drew the most significance. This technique is used across various domains like weather forecasting, sales prediction, marketing applications, and many more. Though ML in healthcare is not so progressive due to the medical complexities involved and data scarcity. But with the advancement in technology, there is a scope of its development soon.

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