Abstract:
"Peripheral Sensory Neuropathy (PSN) is a serious complication of Type 2 Diabetes Mellitus (T2DM) that can lead to severe health issues, including foot ulceration and amputation. This research aims to develop a predictive model to identify risk factors for PSN among T2DM patients in Ratmalana, Sri Lanka, using data collected from the ABC Diabetes Clinic. The study will involve 350 T2DM patients and will analyze variables such as age, gender, smoking history, alcohol consumption, blood sugar control (HbA1c levels), family history of diabetes, and diabetes duration.
Advanced machine learning techniques and data analysis will be employed to detect early signs of PSN and provide insights for healthcare interventions. The methodology includes data preprocessing, feature selection using the Chi-square method, and model evaluation through metrics like Area Under the Receiver Operating Characteristic Curve (AUC) and F1-Score.
The research is structured into eight chapters, starting with an introduction to PSN's significance in T2DM and the research objectives. A literature review follows, synthesizing existing knowledge and identifying gaps. Methodology details data collection, preprocessing, and model development. Subsequent chapters focus on feature engineering, machine learning model selection, training/testing phases, and ethical considerations in diabetes prediction research.
The findings are expected to enhance early PSN detection and risk factor identification among T2DM patients, enabling timely interventions that improve quality of life. This research will also provide a framework for expansion to other healthcare facilities and regions, contributing significantly to diabetes management and policy-making efforts aimed at reducing the burden of PSN in similar contexts"
