Machine learning in healthcare is useful in detecting diabetes in patients while also being instrumental in diabetes management.
Diabetes is not known as the silent killer of diseases for nothing. It is notoriously hard to detect, with approximately 46% of patients remaining completely undiagnosed and untreated. It is also an ailment that stays with patients for several years, even, in many cases, throughout their lifetimes from the point of diagnosis. As a result, it is one of the world’s most researched, dissected and closely analyzed diseases. The global cost of diabetes is already incredibly high and is expected to grow even further in the next decade.
Diabetes is one of the leading causes of heart problems, blindness, hearing impairment and Alzheimer’s disease in patients. Also, people with diabetes are more likely to have severe health complications from COVID-19 than others. Due to all these reasons, it is necessary that diabetes is detected early before using adequate medication and diet management to aid patients.
The involvement of machine learning in healthcare allows doctors to aid diabetic patients much more effectively in the following ways:
Early Diabetes Detection
Currently, the standard procedure for diabetic diagnosis is through multiple tests before assessing appropriate treatments are considered. Such detection tests may not always be accurate. Therefore, big data analytics is a useful tool for the purpose of diabetic detection. Today, the concept of wearable health monitoring tech is handy for individuals to monitor their sleep patterns, calories and oxygen levels. Healthcare companies such as Fitbit are currently working with third-party collaborators to create tools that allow its wearer to know their blood sugar level based on factors such as BMI, age and dietary patterns.
As stated above, early detection must be done at an early stage as later patients may suffer partial or total vision impairment due to diabetes-accelerated retinopathy. Tools based on AI, deep learning and machine learning in healthcare can recommend such patients to visit an ophthalmologist as soon as early signs of retinopathy are found in their eyes.
Effective Diabetes Management
While there is no definitive cure for diabetes, there are multiple machine learning and AI-based methods that can come in handy for blood sugar management in patients. Based on the readings from machine learning-based blood glucose detectors—which do not need patients to take a fingerprick test for collecting data—AI healthcare tools can recommend changes in patient diet and care programs to manage their diabetes more effectively. AI-based nutrition coaching tools suggest meal options based on readings from wearable data-capturing gadgets used by diabetic patients.
Diabetes management depends on how patients and doctors use the vast amounts of blood sugar data captured by AI-based devices. As we can see, AI and machine learning can be useful to prolong the life expectancy of diabetics through continuous monitoring and management measures.
