The 21st century has been an era of data-driven decisions. It is said that the segments or industries that generate more data will grow faster and the organizations that utilize this data to make important decisions will be ahead of the curve.
When it comes to industries that generate huge data, healthcare is one among the top thanks to the several new methods of data collection, such as sensor-generated data.
Machine learning allows building models to quickly analyze data and deliver results, leveraging both historical and real-time data. With machine learning, healthcare service providers can make better decisions on patient’s diagnoses and treatment options, which leads to the overall improvement of healthcare services.
Previously, it was challenging for healthcare professionals to collect and analyze the huge volume of data for effective predictions and treatments since there were no technologies or tools available. Now, with machine learning, it’s been relatively easy, as big data technologies such as Hadoop are mature enough for wide-scale adoption. In fact, 54% of organizations are using or considering Hadoop as big data processing tool to get important insights on healthcare, according to the Ventana Research Survey. 94% of Hadoop users perform analytics on voluminous data that they believe was not possible before.
Machine learning algorithms can also be helpful in providing vital statistics, real-time data, and advanced analytics in terms of the patient’s disease, lab test results, blood pressure, family history, clinical trial data, and more to doctors.
What if this data could be used predict a certain disease or the risk of developing a disease?
As healthcare generates large data, the challenge is to collect this data and effectively use it for analysis, prediction, and treatment. Let's see how machine learning can address this challenge.
The modern approach to healthcare is to prevent the disease with early intervention rather than go for a treatment after diagnosis. Traditionally, physicians or doctors use a risk calculator to assess the possibility of disease development. These calculators use fundamental information such as demographics, medical conditions, life routines, and more to calculate the probability of developing a certain disease. Such calculations are done using equation-based mathematical methods and tools. The challenge here is the low accuracy rate with a similar equation-based approach.
For an example, the Framingham Study can predict the hospitalization with only 56% of accuracy for a long-term cardiovascular disease.
But with recent development in technologies such as big data and machine learning, it's possible to get more accurate results for disease prediction. Physicians are teaming up with statisticians and computer scientists to develop better tools to predict the diseases. Experts in the field are working on the methodologies to identify, develop, and fine-tune machine learning algorithms and models that can deliver accurate predictions.
To develop a strong and more accurate machine learning model, we can use data collected from studies, patient demographics, medical health records, and other sources.
The difference between traditional approach and the machine learning approach for disease prediction is the number of dependent variables to consider. In a traditional approach, very few variables are considered, such as age, weight, height, gender, and more (due to computational limitation). On the other hand, machine learning being processed on computing devices can consider a large number of variables, which results in a better accuracy of healthcare data.
According to a recent study, the researcher obtained better diagnostic accuracy, using entire medical records by considering around 200 variables.
Apart from disease prediction, there are the few more potential areas like drug discovery or electronic health records where machine learning can improve healthcare industry. We see, with machine learning applications, the healthcare and medicine segment can advance into a new realm and completely transform healthcare operations.
Article source: Healthcare and Machine Learning: The Future with Possibilities