Volume 11 Issue 2
Utilizing Artificial Intelligence for Early Detection of Diabetes-Related Neuropathy: A Machine Learning Approach to Predict and Prevent Peripheral Nerve Damage in Diabetes Patients
1Ali Raza, 2Dr. Shama Iqbal, 3Dr Muhammad Kamran Tariq, 4Kashif Lodhi, 5Dr. Syed Tariq Ali Adnan
1PIMS, Islamabad
2Associate Professor Physiology Sheikh Zayed Medical College Rahim Yar Khan
3SR Medicine, Shahida Islam Medical Complex, Lodhran, Pakistan
4Department of Agricultural, Food and Environmental Sciences. Università Politécnica delle Marche Via Brecce Bianche 10, 60131 Ancona (AN) Italy
5Assistant professor community medicine, Karachi medical and dental college. KMC
ABSTRACT
Background: Diabetes-related neuropathy poses a significant challenge in managing diabetes, leading to peripheral nerve damage and subsequent complications. Early detection of neuropathy is crucial for preventing further deterioration and improving patient outcomes. Traditional diagnostic methods lack sensitivity and specificity, necessitating the exploration of advanced technologies like artificial intelligence (AI) to enhance detection accuracy.
Aim: This study aimed to develop a machine learning model leveraging AI techniques to predict and prevent peripheral nerve damage in diabetes patients by early detection of neuropathy.
Methods: We collected data from diabetes patients, including clinical parameters and diagnostic tests related to neuropathy. Feature selection techniques were applied to identify the most relevant predictors for neuropathy. Subsequently, various machine learning algorithms were trained and tested on the dataset to develop a predictive model. The performance of each model was evaluated using metrics such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC).
Results: Our machine learning approach achieved promising results in predicting the onset of diabetes-related neuropathy. The developed model demonstrated high accuracy, sensitivity, and specificity in distinguishing between diabetic patients at risk of developing neuropathy and those with stable neuropathy status. Moreover, the model identified key clinical variables associated with neuropathy progression, facilitating early intervention and personalized treatment strategies.
Conclusion: The application of AI-based machine learning approaches holds great potential for early detection and prevention of diabetes-related neuropathy. By accurately identifying individuals at risk of peripheral nerve damage, timely interventions can be initiated to mitigate the progression of neuropathic symptoms and improve patient outcomes. Further refinement and validation of the model on larger and diverse datasets are warranted to enhance its clinical utility.
Keywords: Artificial intelligence, Machine learning, Diabetes, Neuropathy, Peripheral nerve damage, Early detection, Predictive model, Preventive healthcare.