Machine Learning with Neural Networks for Early Detection of Peripheral Neuropathy in Patients with Diabetes

A key difficulty in healthcare is the often-delayed diagnosis and incapacitating effects of peripheral neuropathy, a frequent complication of diabetes. In this research, We examine the viability of employing neural networks and machine learning for screening diabetes patients for peripheral neuropathy. Training, evaluating, hyperparameter tweaking, and ethical concerns are only some of the steps in the approach that follow data gathering, feature engineering, and the creation of a neural network architecture. To show that this method can work, we utilise hypothetical data. The findings show the possibility for precise early diagnosis, personalised therapy, and lower healthcare expenditures. Ethical concerns highlight the need of proper data handling. This study connects the dots between AI and healthcare, providing useful information for practitioners, academics, and policymakers. Patients would be more in control and have a better quality of life if this were to happen. Technological progress, real-time monitoring with wearable devices, patient-centric initiatives, multidisciplinary cooperation, cost-effectiveness, and policy ramifications are all things to keep in mind as we go forward. This study lays the groundwork for revolutionary improvements in diabetes care by opening the door to preventative measures and individualized treatment plans.