Electronically stored medical imaging data is plentiful and Machine Learning algorithms can be fed with this type of dataset, to detect and uncover patterns and anomalies. In this article, I will introduce you to five machine learning projects for healthcare.
Machines and algorithms can interpret imaging data just as a highly trained radiologist could identify suspicious spots on the skin, lesions, tumours and bleeding in the brain. The use of machine learning tools and platforms to help radiologists is therefore poised to grow exponentially.
Also, Read – Analyze Call Records with Machine Learning using Google Cloud Platform.
Machine Learning Projects for Healthcare
Machine learning is used in many spheres around the world. The healthcare industry is no exception. Machine learning can play a critical role in predicting the presence/absence of locomotor disorders, heart disease, cancer, lungs disease and more.
Such information, if predicted well in advance, can provide important information to physicians who can then tailor their diagnosis and treatment per patient. Now let’s take a look at some machine learning projects for healthcare.
Heart Disease Prediction:
Here you will learn about the emerging possibilities of heart disease. The results of this machine learning model will provide the odds of heart disease occurring in percentage terms. See this machine learning based healthcare project here.
Skin Cancer Classification:
Skin cancer is one of the most common types of disease in the United States. Up to 4 million cases were reported dead due to skin cancer in the United States during the year. Here you will learn how to create a skin cancer classification model with machine learning.
That’s a huge number, really 4 million, people have died from just skin cancer in one country. Like all of these people were dying, but half of these cases or maybe, even more, did not go to the doctor in the early stages of the disease when it could have been prevented. See this machine learning based healthcare project here.
Lung Segmentation:
Lung segmentation is one of the most useful tasks of machine learning in healthcare. Lung CT image segmentation is an initial step necessary for lung image analysis, it is a preliminary step to provide accurate lung CT image analysis such as detection of lung cancer.
Dicom is the de-facto repository in medical imaging. These files contain a lot of metadata. This analysis pixel size/coarseness differs from analysis to analysis, which can adversely affect the performance of CNN approaches. See this machine learning based healthcare project here.
Predict Diabetes:
About one in seven American adults currently has diabetes, according to the Centers for Disease Control and Prevention. But by 2050, that rate could skyrocket to one in three. With that in mind, here’s what we’ll learn here: Learn to use Machine Learning to help us predict diabetes. See this machine learning based healthcare project here.
Contact Tracing:
Contact tracing is a process used by public health ministries to help stop the spread of infectious disease, such as COVID-19, within a community. Once a person is positive for coronavirus, it is very important to identify other people who may have been infected by the patients diagnosed.
To identify infected people, the authorities follow the activity of patients diagnosed in the last 14 days. This process is called contact tracking. Depending on the country and the local authority, the search for contacts is carried out either by manual methods or by numerical methods. See this machine learning project on healthcare from here.
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