Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems
Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-05-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858282/full |
| _version_ | 1817992992225492992 |
|---|---|
| author | Sashikala Mishra Kailash Shaw Debahuti Mishra Shruti Patil Ketan Kotecha Satish Kumar Simi Bajaj |
| author_facet | Sashikala Mishra Kailash Shaw Debahuti Mishra Shruti Patil Ketan Kotecha Satish Kumar Simi Bajaj |
| author_sort | Sashikala Mishra |
| collection | DOAJ |
| description | Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates. |
| first_indexed | 2024-04-14T01:33:49Z |
| format | Article |
| id | doaj.art-4cb7da84071b4351b89da8f6ec8bdbed |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-14T01:33:49Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-4cb7da84071b4351b89da8f6ec8bdbed2022-12-22T02:20:04ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-05-011010.3389/fpubh.2022.858282858282Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI SystemsSashikala Mishra0Kailash Shaw1Debahuti Mishra2Shruti Patil3Ketan Kotecha4Satish Kumar5Simi Bajaj6Symbiosis Institute of Technology, Symbiosis International University, Pune, IndiaSymbiosis Institute of Technology, Symbiosis International University, Pune, IndiaDepartment of Computer Science and Engineering, Siksha O Anusandhan Deemed to be University, Bhubaneshwar, IndiaSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSchool of Computer Data and Mathematical Sciences, University of Western Sydney, Sydney, NSW, AustraliaHealthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.https://www.frontiersin.org/articles/10.3389/fpubh.2022.858282/fullbit-fusion ensemble algorithmclassifier fusionk-nearest neighborMulti-Layer PerceptronNaïve Bayesian classifiersupport vector machine |
| spellingShingle | Sashikala Mishra Kailash Shaw Debahuti Mishra Shruti Patil Ketan Kotecha Satish Kumar Simi Bajaj Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems Frontiers in Public Health bit-fusion ensemble algorithm classifier fusion k-nearest neighbor Multi-Layer Perceptron Naïve Bayesian classifier support vector machine |
| title | Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems |
| title_full | Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems |
| title_fullStr | Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems |
| title_full_unstemmed | Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems |
| title_short | Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems |
| title_sort | improving the accuracy of ensemble machine learning classification models using a novel bit fusion algorithm for healthcare ai systems |
| topic | bit-fusion ensemble algorithm classifier fusion k-nearest neighbor Multi-Layer Perceptron Naïve Bayesian classifier support vector machine |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858282/full |
| work_keys_str_mv | AT sashikalamishra improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT kailashshaw improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT debahutimishra improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT shrutipatil improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT ketankotecha improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT satishkumar improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems AT simibajaj improvingtheaccuracyofensemblemachinelearningclassificationmodelsusinganovelbitfusionalgorithmforhealthcareaisystems |