| Type of Material: | Thesis |
| Title: | Enhancing classification accuracy of healthcare data for disease diagnosis using machine learning |
| Researcher: | Patra, Radhanath |
| Guide: | Khuntia, Bonomali | Panda, Dhurba Charan |
| Department: | Department of Electronic Sciences |
| Publisher: | Berhampur University, Berhampur |
| Place: | Berhampur |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Electronic Science | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronic Sciences, Berhampur University, Berhampur, Berhampur; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 17:18:08 | |
| 008 | __ | 261014t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464220 |
| 040 | __ | |dIN-AhILN|aBRMP_760007 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aPatra, Radhanath |
| 110 | __ | |aDepartment of Electronic Sciences|0U-0350|dBerhampur|bBerhampur University, Berhampur|ein |
| 245 | __ | |aEnhancing classification accuracy of healthcare data for disease diagnosis using machine learning |
| 260 | __ | |aBerhampur|c2023|bBerhampur University, Berhampur |
| 300 | __ | |a160p.|dCD |
| 502 | __ | |cDepartment of Electronic Sciences, Berhampur University, Berhampur, Berhampur|d2023|bPhD |
| 518 | __ | |oDate of Award|d2024 |
| 518 | __ | |d2017|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |aUtilizing computer-aided diagnosis for the detection of diseases has established itself as a reliable and effective approach. Our research is dedicated to the identification of critical health conditions, including cardiovascular disorders, lung cancer, breast cancer, and diabetes, employing a diverse array of cutting-edge machine-learning techniques. Our study introduces innovative methods that encompass data preprocessing, meaningful feature selection, customized algorithms, hybrid classification models, and state-of-the-art optimization strategies. The research journey commences by placing a strong emphasis on feature engineering and classification. Within this initial phase, the key processes such as data imputation, normalization, and information gain assessment are highlighted. Furthermore, a hybrid optimization algorithm known as the Henry Sea Lion Optimization (HSLnO) is devised from Henry Gas Solubility Optimization (HGSO), and Sea Lion Optimization (SLnO) for the feature selection proces |
| 650 | __ | |aElectronic Science|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aKhuntia, Bonomali|eGuide |
| 700 | __ | |aPanda, Dhurba Charan|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/661982|yShodhganga |
| 905 | __ | |afromsg |
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