Diabetic nephropathy is a critical condition that can lead to irreversible kidney damage. Chronic kidney disease consists of the gradual loss of kidney function, which greatly decreases people's quality of life and reduces average life expectancy. Thus, the first signs of diabetic nephropathy are critical to prevent or slow the disease's progression. The project in question aims to predict the likelihood of disease development, allowing medical teams to act proactively and prevent progression. According to the Portuguese Society of Nephrology, one third of people with diabetes develop chronic kidney disease. In addition, about a third of patients who start dialysis treatment do so because of a kidney complication related to diabetes. In addition to the impact on the patient's quality of life, there are significant financial and social costs associated with treatment.
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The project, developed in partnership between the multidisciplinary team of Nova SBE Data Science Knowledge Center and the clinical staff of APDD, aims to develop a prototype of a predictive model that is able to identify the risk factors of a patient with type 2 diabetes mellitus II come to develop diabetic nephropathy. The tool will inform the medical team that, with this knowledge, they will be able to better guide their patients in order to prevent or delay the onset of the disease or even delay its evolution.

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