| Type of Material: | Thesis |
| Title: | Non invasive system for heart disease prediction |
| Researcher: | Baviskar, Vaishali |
| Guide: | Verma, Madhushi | Chatterjee, Pradeep |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Heart--Diseases | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 14:54:25 | |
| 008 | __ | 261014t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464166 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aBaviskar, Vaishali |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aNon invasive system for heart disease prediction |
| 260 | __ | |aGreater Noida|c2023|bBennett University |
| 300 | __ | |axvii; 184p. |
| 502 | __ | |d2023|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |d2018|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |aquotHeart disease (HD) is considered as a potentially fatal disease. Because of multiple contributory risk factors such as abnormal pulse rate, diabetes, high blood pressure, excessive cholesterol, and so on, identifying the condition is difficult. Accurate and timely diagnosis is critical for HD therapy and prevention. As HD is an important reason of deaths in developing nations, the intentions of detecting the risk factors and early sign detection of this disease is important for which the present study has been undertaken. In spite of the various endeavors of conventional works, there is a scope for improvement with regard to accuracy. To attain high prediction accuracy, as per defined objective 1, in phase-I, the proposed work uses genetic algorithm (GA), particle swarm optimisation (PSO), african buffalo optimisation (ABO) and genetic sine algorithm (GSA) on the benchmark UCI dataset to remove the unnecessary and redundant features and prevent the features from becoming trapped in local minima. For the |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aHeart--Diseases |
| 700 | __ | |aVerma, Madhushi|eGuide |
| 700 | __ | |aChatterjee, Pradeep|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/573417|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.