Research Publications

Now showing 1 - 4 of 4

Open Access
Personalized Health Monitoring System to Track and Visualize Serum Creatinine Levels of Chronic Kidney Disease Patients: Creatinine Care
(SLIIT City UNI, 2025-07-08) Fernando, N.R.T; Jayaweera, Y.D
Creatinine Care is a mobile application developed to monitor and manage serum creatinine levels in chronic kidney disease patients. Chronic kidney disease is a significant global health issue affecting both adults and children. Many patients are unaware of their kidney health status, leading to sudden spikes in creatinine levels and emergency hospitalizations. Serum creatinine is a critical biomarker used in estimating kidney function, particularly through the glomerular filtration rate formula. However, most existing kidney-related applications focus on general awareness, basic health tracking, and diet plans, without offering specific creatinine-level monitoring or paediatric support. This application addresses these gaps by offering a dedicated platform for both adult and child kidney patients to track creatinine levels over time. Key features include digital report storage, automated data extraction, visual trend analysis, checkup reminders, and personalized recommendations based on the base creatinine level. The system is developed using React Native with Expo Go for the frontend and SQLite for local storage. A Node.js Express backend supports Optical Character Recognition through Tesseract.js for extracting data from scanned reports. Evaluation involved user acceptance testing and text extraction accuracy testing. The Optical Character Recognition achieved a word-level accuracy of 93.33% on high-quality images and 76.92% on low-quality images, with an overall upload success rate above 86%. The results demonstrate the system's effectiveness in reducing manual data entry, improving patient awareness, and supporting real-time monitoring. Creatinine Care introduces a novel, allin- one digital tool for personalized chronic kidney disease management in paediatric and adult patients.
Embargo
Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)
(IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.
Embargo
Dietary prediction for patients with Chronic Kidney Disease (CKD) by considering blood potassium level using machine learning algorithms
(IEEE, 2017-12-13) Wickramasinghe, M. P. N. M; Perera, D. M; Kahandawaarachchi, K. A. D. C. P
Kidney damage and diminished function that lasts longer than three months is known as Chronic Kidney Disease (CKD). The primary goal of this research study is to identify the suitable diet plan for a CKD patient by applying the classification algorithms on the test result obtained from patients' medical records. The aim of this work is to control the disease using the suitable diet plan and to identify that suitable diet plan using classification algorithms. The suggested work pacts with the recommendation of various diet plans by using predicted potassium zone for CKD patients according to their blood potassium level. The experiment is performed on different algorithms like Multiclass Decision Jungle, Multiclass Decision Forest, Multiclass Neural Network and Multiclass Logistic Regression. The experimental results show that Multiclass Decision Forest algorithm gives a better result than the other classification algorithms and produces 99.17% accuracy.
Embargo
Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD)
(IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.

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