ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application
Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictiv...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000105 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846122139211530240 |
|---|---|
| author | Rajib Kumar Halder Mohammed Nasir Uddin Md. Ashraf Uddin Sunil Aryal Sajeeb Saha Rakib Hossen Sabbir Ahmed Mohammad Abu Tareq Rony Mosammat Farida Akter |
| author_facet | Rajib Kumar Halder Mohammed Nasir Uddin Md. Ashraf Uddin Sunil Aryal Sajeeb Saha Rakib Hossen Sabbir Ahmed Mohammad Abu Tareq Rony Mosammat Farida Akter |
| author_sort | Rajib Kumar Halder |
| collection | DOAJ |
| description | Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/ |
| format | Article |
| id | doaj-art-d18e42cf0e1e4c8f8f0d40dfab46fc4f |
| institution | Kabale University |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-d18e42cf0e1e4c8f8f0d40dfab46fc4f2024-12-15T06:15:13ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100371ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web applicationRajib Kumar Halder0Mohammed Nasir Uddin1Md. Ashraf Uddin2Sunil Aryal3Sajeeb Saha4Rakib Hossen5Sabbir Ahmed6Mohammad Abu Tareq Rony7Mosammat Farida Akter8Dept. of Computer Science and Engineering, Jagannath University, Dhaka 1100, BangladeshDept. of Computer Science and Engineering, Jagannath University, Dhaka 1100, BangladeshSchool of Information Technology, Deakin University, Geelong 3125, Australia; Corresponding author.School of Information Technology, Deakin University, Geelong 3125, AustraliaDept. of Computer Science and Engineering, Jagannath University, Dhaka 1100, BangladeshDept. of Cyber Security, Bangabandhu Sheikh Mujibur Rahman Digital University, Kaliakoir, Gazipur 1750, BangladeshDept. of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh; School of Information Technology, Deakin University, Geelong 3125, Australia; Dept. of Cyber Security, Bangabandhu Sheikh Mujibur Rahman Digital University, Kaliakoir, Gazipur 1750, Bangladesh; Dept. of Educational Technology, Bangabandhu Sheikh Mujibur Rahman Digital University, Kaliakoir, Gazipur 1750, Bangladesh; Dept. of Statistics, Noakhali Science and Technology University, Noakhali 3814, BangladeshDept. of Statistics, Noakhali Science and Technology University, Noakhali 3814, BangladeshDept. of Computer Science and Engineering, Jagannath University, Dhaka 1100, BangladeshChronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/http://www.sciencedirect.com/science/article/pii/S2153353924000105Chronic kidney diseasesMachine learningFeature selectionClassification |
| spellingShingle | Rajib Kumar Halder Mohammed Nasir Uddin Md. Ashraf Uddin Sunil Aryal Sajeeb Saha Rakib Hossen Sabbir Ahmed Mohammad Abu Tareq Rony Mosammat Farida Akter ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application Journal of Pathology Informatics Chronic kidney diseases Machine learning Feature selection Classification |
| title | ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application |
| title_full | ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application |
| title_fullStr | ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application |
| title_full_unstemmed | ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application |
| title_short | ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application |
| title_sort | ml ckdp machine learning based chronic kidney disease prediction with smart web application |
| topic | Chronic kidney diseases Machine learning Feature selection Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000105 |
| work_keys_str_mv | AT rajibkumarhalder mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT mohammednasiruddin mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT mdashrafuddin mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT sunilaryal mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT sajeebsaha mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT rakibhossen mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT sabbirahmed mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT mohammadabutareqrony mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication AT mosammatfaridaakter mlckdpmachinelearningbasedchronickidneydiseasepredictionwithsmartwebapplication |