Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods
The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing valu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/2/92 |
| _version_ | 1797620304805101568 |
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| author | Ashokkumar Palanivinayagam Robertas Damaševičius |
| author_facet | Ashokkumar Palanivinayagam Robertas Damaševičius |
| author_sort | Ashokkumar Palanivinayagam |
| collection | DOAJ |
| description | The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications. |
| first_indexed | 2024-03-11T08:39:16Z |
| format | Article |
| id | doaj.art-0098f5b92655401ea972e1b9af2fb183 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T08:39:16Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-0098f5b92655401ea972e1b9af2fb1832023-11-16T21:12:13ZengMDPI AGInformation2078-24892023-02-011429210.3390/info14020092Effective Handling of Missing Values in Datasets for Classification Using Machine Learning MethodsAshokkumar Palanivinayagam0Robertas Damaševičius1Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, IndiaDepartment of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, LithuaniaThe existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.https://www.mdpi.com/2078-2489/14/2/92diabetes classificationmissing valuesdata imputationfalse rate reductiontwo-level classification |
| spellingShingle | Ashokkumar Palanivinayagam Robertas Damaševičius Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods Information diabetes classification missing values data imputation false rate reduction two-level classification |
| title | Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods |
| title_full | Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods |
| title_fullStr | Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods |
| title_full_unstemmed | Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods |
| title_short | Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods |
| title_sort | effective handling of missing values in datasets for classification using machine learning methods |
| topic | diabetes classification missing values data imputation false rate reduction two-level classification |
| url | https://www.mdpi.com/2078-2489/14/2/92 |
| work_keys_str_mv | AT ashokkumarpalanivinayagam effectivehandlingofmissingvaluesindatasetsforclassificationusingmachinelearningmethods AT robertasdamasevicius effectivehandlingofmissingvaluesindatasetsforclassificationusingmachinelearningmethods |