- Tahsinur Rahman
- Aniqa Zaida Khanom
- Sheikh Mastura Farzana
This thesis project was done as part of the Computer Science and Engineering curriculum of BRAC University, Dhaka, Bangladesh. It presents a comparative analysis of different Machine Learning algorithms in predicting health complications that are induced by Diabetes Mellitus. The thesis was conducted in a group of three students under the guidance of a supervising faculty member and a co-supervisor.
The research paper written on the topic was titled "Analysis of Linear and Non-Linear Classifiers in Imbalanced Data to Predict Diabetes Induced Complications". It was presented at The 15th International Conference on Machine Learning and Data Mining MLDM 2019, held in New York, USA. The manuscript of the paper is available in the 'MLDM Conference Paper' folder. The manuscript of the thesis report and relevant codes are available in this repository.
Diabetes Mellitus is a medical condition of the Pancreas in which the body‘s ability to produce or respond to the hormone, Insulin, diminishes. As a result, over time it damages other organs in the body- primarily Kidney, Liver, Eyes, Heart and Brain. This proposed model uses time series data of a year that contains 164 features including results of different pathological tests. Various machine learning algorithms are used to predict the probability of Diabetes induced Nephropathy and Cardiovascular disease.
For implementation Python was used as the primary language with few scripts in the R programming language. Python libraries like pandas, numpy,scikit-learn, matplotlib, etc. were also used.