Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis
CKD (chronic kidney disease) have been identified as a serious public health concern globally. Machine learning models can successfully enable physicians to reach this aim because of their rapid and accurate identification performance. In this paper, KNN (K Nearest Neighbor) imputations, which choos...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266591742300051X |
| _version_ | 1797797134231142400 |
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| author | T. Saroja Y. Kalpana |
| author_facet | T. Saroja Y. Kalpana |
| author_sort | T. Saroja |
| collection | DOAJ |
| description | CKD (chronic kidney disease) have been identified as a serious public health concern globally. Machine learning models can successfully enable physicians to reach this aim because of their rapid and accurate identification performance. In this paper, KNN (K Nearest Neighbor) imputations, which choose multiple full samples with the most comparable values for replacing missing values have been utilized in this work. Additionally, conventional MLT (Machine Learning Technique) need to produce better outcomes than DLT (Deep Learning technique). The prediction step may be sluggish, sensitive to the size of the data, and filled with irrelevant information when there is a lot of data. Tensor factorization and ANFIS (Adaptive Neuro-Fuzzy Inference System) are added for missing data imputations to address this issue. The technique for addressing feature selections is called AWDBOA (Adaptive Weight Dynamic Butterfly Optimization Algorithm), inspired by nature. At the same time, adjustable weights for feature selection from the dataset are added. A modified form of NN called NWCNN (Novel Weight Convolution Neural Network) classifier uses convolution rather than standard matrix multiplication in at least one of its layers. Convolution layers are hidden layers in NWCNN, and kernel functions improve or fine-tune the classifier's parameters. The dataset of CKD was imperturbable from UCI (University of California, Irvine) ML repository and had a large number of misplaced values. This work's proposed technique is evaluated in terms of precision, recall, F1-score, sensitivitiy, specificity, and accuracy with the values of 99.17%, 98.71%, 98.94%, 98.71%, 99.10%, and 99.04% were obtained which is higher than the other models. |
| first_indexed | 2024-03-13T03:43:35Z |
| format | Article |
| id | doaj.art-7666919027ee431db0684ca6bc87de04 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-13T03:43:35Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-7666919027ee431db0684ca6bc87de042023-06-23T04:44:07ZengElsevierMeasurement: Sensors2665-91742023-06-0127100715Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosisT. Saroja0Y. Kalpana1Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai - 600 117, India; Corresponding author.Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai - 600 117, IndiaCKD (chronic kidney disease) have been identified as a serious public health concern globally. Machine learning models can successfully enable physicians to reach this aim because of their rapid and accurate identification performance. In this paper, KNN (K Nearest Neighbor) imputations, which choose multiple full samples with the most comparable values for replacing missing values have been utilized in this work. Additionally, conventional MLT (Machine Learning Technique) need to produce better outcomes than DLT (Deep Learning technique). The prediction step may be sluggish, sensitive to the size of the data, and filled with irrelevant information when there is a lot of data. Tensor factorization and ANFIS (Adaptive Neuro-Fuzzy Inference System) are added for missing data imputations to address this issue. The technique for addressing feature selections is called AWDBOA (Adaptive Weight Dynamic Butterfly Optimization Algorithm), inspired by nature. At the same time, adjustable weights for feature selection from the dataset are added. A modified form of NN called NWCNN (Novel Weight Convolution Neural Network) classifier uses convolution rather than standard matrix multiplication in at least one of its layers. Convolution layers are hidden layers in NWCNN, and kernel functions improve or fine-tune the classifier's parameters. The dataset of CKD was imperturbable from UCI (University of California, Irvine) ML repository and had a large number of misplaced values. This work's proposed technique is evaluated in terms of precision, recall, F1-score, sensitivitiy, specificity, and accuracy with the values of 99.17%, 98.71%, 98.94%, 98.71%, 99.10%, and 99.04% were obtained which is higher than the other models.http://www.sciencedirect.com/science/article/pii/S266591742300051XAdaptive weight dynamic butterfly optimization algorithmDeep learningK-Nearest neighborNovel weight convolution neural networksFuzzy logic |
| spellingShingle | T. Saroja Y. Kalpana Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis Measurement: Sensors Adaptive weight dynamic butterfly optimization algorithm Deep learning K-Nearest neighbor Novel weight convolution neural networks Fuzzy logic |
| title | Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| title_full | Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| title_fullStr | Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| title_full_unstemmed | Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| title_short | Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| title_sort | hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis |
| topic | Adaptive weight dynamic butterfly optimization algorithm Deep learning K-Nearest neighbor Novel weight convolution neural networks Fuzzy logic |
| url | http://www.sciencedirect.com/science/article/pii/S266591742300051X |
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