Analyzing the Performance of Machine Learning Techniques in Disease Prediction
The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/7529472 |
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| _version_ | 1832548641869398016 |
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| author | Khongdet Phasinam Tamal Mondal Dony Novaliendry Cheng-Hong Yang Chiranjit Dutta Mohammad Shabaz |
| author_facet | Khongdet Phasinam Tamal Mondal Dony Novaliendry Cheng-Hong Yang Chiranjit Dutta Mohammad Shabaz |
| author_sort | Khongdet Phasinam |
| collection | DOAJ |
| description | The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies. |
| format | Article |
| id | doaj-art-dd71d76e87ca46858f09afb4e1f662e5 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-dd71d76e87ca46858f09afb4e1f662e52025-02-03T06:13:36ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/7529472Analyzing the Performance of Machine Learning Techniques in Disease PredictionKhongdet Phasinam0Tamal Mondal1Dony Novaliendry2Cheng-Hong Yang3Chiranjit Dutta4Mohammad Shabaz5Faculty of Food and Agricultural TechnologyDepartment of BotanyDepartment of Electronic EngineeringDepartment of Electronic EngineeringFaculty of Engineering and TechnologyArba Minch UniversityThe history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies.http://dx.doi.org/10.1155/2022/7529472 |
| spellingShingle | Khongdet Phasinam Tamal Mondal Dony Novaliendry Cheng-Hong Yang Chiranjit Dutta Mohammad Shabaz Analyzing the Performance of Machine Learning Techniques in Disease Prediction Journal of Food Quality |
| title | Analyzing the Performance of Machine Learning Techniques in Disease Prediction |
| title_full | Analyzing the Performance of Machine Learning Techniques in Disease Prediction |
| title_fullStr | Analyzing the Performance of Machine Learning Techniques in Disease Prediction |
| title_full_unstemmed | Analyzing the Performance of Machine Learning Techniques in Disease Prediction |
| title_short | Analyzing the Performance of Machine Learning Techniques in Disease Prediction |
| title_sort | analyzing the performance of machine learning techniques in disease prediction |
| url | http://dx.doi.org/10.1155/2022/7529472 |
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