Topic of Research Seminar: Diagnosis and Prognosis of Diabetes Mellitus with Deep Learning

Abstract: Diabetes being a metabolic disease is affecting people worldwide and brings them under the life risk often. Early diagnosis and prognosis is required to control the disease’s associated health problems and complications to prevent the damage of internal organs that may become fatal to life. In this research efficient deep learning models naming: XGBOOST and LGBM have been implemented on the dataset provided during WiDS 2021 Datathone. Before applying deep learning algorithms, an extensive feature engineering process was
administered to get better insight into correlated features. It highlighted the concerns including filling missing values, class imbalanced, and age groups as important participatory factors in predicting anomalous results. Data segregation on groups including gender, age, ethnicity, and max glucose didn’t show visible differences among the classes so the machine learning procedures were implemented on the complete dataset. Finally, model evaluation was carried out using ROC evaluation method with 0.87 accuracy.

Subject field of Topic: Diagnosis and Prognosis of disease with Deep Learning

Name of Speaker: Erum Afzal

Professional Rank of Speaker: PhD Student

University Email of Speaker: [email protected]

Affiliation of Speaker: Department of Design & Manufacturing Engineering, NUST School of Mechanical & Manufacturing Engineering (NUST-SMME)

Date and Venue: November 23, 2022, 1700 – 1800 hrs, Seminar Hall, School of Mechanical & Manufacturing Engineering (SMME), NUST Islamabad Campus