Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2021
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/14964 |
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10361-149642022-01-26T10:21:45Z Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease Amin, M.M. Shahriar Gomes, Partho Mark Gomes, Jui Philomina Tasneem, Faiza Islam, Md. Saiful Department of Computer Science and Engineering, Brac University Early Diabetes Prediction Diabetes Kidney Disease Prediction Machine Learning Diabetes GBDT Bioinformatics Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-55). Machine Learning has gotten attention in the healthcare industry for the competences to ameliorate disease prediction. Machine learning has already been used in the health sector. Diabetes can also trigger the permanent loss of kidney function. Diabetic kidney disease (DKD) is one of the most recurrent diabetic micro vascular issues and has become the dominant cause of chronic kidney disease (CKD). It causes steady and permanent loss of kidney function. Kidney damage has been caused by poorly controlled diabetes that can damage the blood vessel clusters in the kidneys. Diabetic kidney damage normally develops over a long period of time. Therefore, there is a need for a machine learning model and application that can effectively predict and track the level of diabetes along with Diabetic kidney disease. In present studies, different classification algorithms such as Logistics Regression, Random Forest, Decision Tree, XGBoost show a notable accuracy to predict the early stage of diabetes. In this paper, our key motive is to find an efficient machine learning model to predict diabetes and diabetic kidney disease (DKD). Since, Disease Prognosis is a sensitive issue, it is not ethical to provide a result without extensive testing. Therefore, we have assessed our model using Recall, F-1 Score, Precision, AUC and also followed some robust evaluation metrics such as ROC, Sensitivity and Specificity to appraise performance of the models from the medical perspective. We are able to obtain an optimized prediction models using LightGBM with an accuracy of 98.75 % on diabetic kidney disease prediction and CatBoost with accuracy of 96.15% on diabetes prediction. We have also proposed a web application using our prognostic machine learning model to predict the result based on user input. This application can be used to predict the initial stage of the diabetes mellitus and diabetic kidney disease which may help to expedite the existing disease medication process. M M Shahriar Amin Partho Mark Gomes Jui Philomina Gomes Faiza Tasneem B. Computer Science 2021-09-03T10:07:49Z 2021-09-03T10:07:49Z 2021 2021-06 Thesis ID 17101048 ID 20241067 ID 17301041 ID 17141011 http://hdl.handle.net/10361/14964 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 55 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Early Diabetes Prediction Diabetes Kidney Disease Prediction Machine Learning Diabetes GBDT Bioinformatics Machine learning. |
| spellingShingle |
Early Diabetes Prediction Diabetes Kidney Disease Prediction Machine Learning Diabetes GBDT Bioinformatics Machine learning. Amin, M.M. Shahriar Gomes, Partho Mark Gomes, Jui Philomina Tasneem, Faiza Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Islam, Md. Saiful |
| author_facet |
Islam, Md. Saiful Amin, M.M. Shahriar Gomes, Partho Mark Gomes, Jui Philomina Tasneem, Faiza |
| format |
Thesis |
| author |
Amin, M.M. Shahriar Gomes, Partho Mark Gomes, Jui Philomina Tasneem, Faiza |
| author_sort |
Amin, M.M. Shahriar |
| title |
Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| title_short |
Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| title_full |
Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| title_fullStr |
Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| title_full_unstemmed |
Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| title_sort |
developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14964 |
| work_keys_str_mv |
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