A Systematic Review of Deep Learning Algorithms for the Early Detection of Chronic Disease

Chronic diseases like cancer, heart failure, diabetes, and liver conditions are the leading causes of death and disability around the world. They develop slowly over time, often hiding their presence until they have caused significant damage. Because catching these illnesses early can save lives and reduce suffering, doctors and scientists are constantly searching for better ways to spot them before symptoms appear. In recent years, a branch of computer science known as deep learning has emerged as a powerful tool for this task. Unlike older computer programs that required humans to manually teach them what to look for, deep learning systems can teach themselves. They do this by studying vast amounts of information, such as medical images, blood test results, or heart rhythm recordings, and learning to recognize subtle patterns that human eyes might miss. This ability to find hidden signals in complex data offers a new hope for predicting disease long before it becomes critical.

A team of researchers from the Chittagong University of Engineering and Technology in Bangladesh set out to understand exactly how far this technology has come. They did not run a single new experiment; instead, they performed a massive, organized search through thousands of existing scientific studies to see what has already been discovered. Following a strict set of rules used to ensure fairness and accuracy in scientific reviews, they sifted through hundreds of papers published between 2018 and 2026. From an initial pool of 800 studies, they carefully selected 55 high-quality reports that focused specifically on using deep learning to detect chronic diseases early. Their goal was to map the landscape of current research, identifying which computer models work best, what data they use, and where the technology still struggles.

The researchers found that the most successful computer models depend heavily on the type of data they are analyzing. When the task involves looking at pictures, such as X-rays, MRI scans, or microscope images of tissue, a specific type of deep learning model called a Convolutional Neural Network is the clear leader. These systems excel at spotting shapes, textures, and boundaries within images. In studies focused on cancer, particularly breast cancer and skin cancer, these image-focused models achieved remarkable success, with some reaching accuracy rates as high as 99.5 percent. For example, one study used a model to analyze thousands of breast tissue images and correctly identified cancer in nearly every case, while another system distinguished between different types of skin tumors with similar precision. These models are becoming so good that they can sometimes see details that are invisible to the naked eye, offering a powerful second opinion for pathologists.

However, not all medical data comes in the form of pictures. Some information, like heartbeats, brain waves, or a patient's medical history over many years, arrives as a sequence of numbers changing over time. For these types of data, different computer architectures are required. The review highlighted that models designed to remember past information, such as Long Short-Term Memory networks, are particularly effective for heart disease and diabetes. These systems can track how a patient's condition evolves, spotting dangerous trends in heart rhythm or blood sugar levels that a single snapshot would miss. In heart disease prediction, specific models like the HQNN achieved an accuracy of 99.8 percent. Similarly, for diabetes, new approaches that combine different types of learning have demonstrated the ability to forecast the onset of the disease, with one study reaching 75 percent accuracy in predicting the start of type 2 diabetes seven years in advance using data ranging from simple blood tests to complex genetic markers.

Despite these impressive numbers, the researchers were careful to point out that the technology is not yet ready to replace doctors in every clinic. A major hurdle is the lack of high-quality, labeled data. To teach a computer to recognize a disease, scientists need thousands of examples where the answer is already known, but such datasets are often small, incomplete, or biased. Furthermore, these powerful computer systems are often "black boxes," meaning they can give a correct answer without explaining why they reached that conclusion. In medicine, knowing the reasoning behind a diagnosis is just as important as the diagnosis itself. The review also noted that many of the most successful studies were tested only in controlled environments or on specific local datasets, and it remains unclear if they would work just as well in different hospitals or with different patient populations.

The authors conclude that while deep learning has proven its potential to revolutionize early disease detection, significant work remains before it can be widely trusted in everyday medical practice. The path forward involves creating larger, more diverse collections of medical data and developing methods that make these computer models more transparent and easier to understand. The review suggests that the future lies in hybrid approaches, where different types of models are combined to handle both images and time-based data simultaneously, and in the use of explainable artificial intelligence that can tell a doctor exactly what it saw to make its decision. Until these challenges are met, deep learning will likely serve best as a sophisticated assistant, helping medical professionals spot the earliest signs of chronic illness and giving patients a better chance at a healthy life.