| Type of Material: | Thesis |
| Title: | development of predictive analytics model for disease prediction using machine learning techniques |
| Researcher: | Chauhan Hetal Bhupendrasinh |
| Guide: | Modi, Kirit J |
| Department: | FACULTY OF ENGINEERING AND TECHNOLOGY |
| Publisher: | Ganpat University |
| Place: | Mehsana |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; FACULTY OF ENGINEERING AND TECHNOLOGY, Ganpat University, Mehsana; 2023; 17276341006 |
| Fulltext: | Shodhganga |
| 000 | 00000ctm a2200000ua 4500 | |
| 001 | 455328 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-26 16:54:14 | |
| 008 | __ | 240924t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_455328 |
| 040 | __ | |aGANU_384012|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aChauhan Hetal Bhupendrasinh|eResearcher |
| 110 | __ | |aFACULTY OF ENGINEERING AND TECHNOLOGY|bGanpat University|dMehsana|ein|0U-0132 |
| 245 | __ | |adevelopment of predictive analytics model for disease prediction using machine learning techniques |
| 260 | __ | |aMehsana|bGanpat University|c2023 |
| 300 | __ | |a3326 kb|dDVD |
| 500 | __ | |aDisease Prediction, Covid-19 diagnosis, Machine learning, Predictive model, Severity Prediction, Deep Learning, Channel Attention, Multi Scale Features |
| 502 | __ | |o17276341006|bPhD|cFACULTY OF ENGINEERING AND TECHNOLOGY, Ganpat University, Mehsana|d2023 |
| 518 | __ | |oDate of Award|d2024 |
| 518 | __ | |oDate of Registration|d2017 |
| 518 | __ | |oDate of Notification|d2023-12-29 |
| 518 | __ | |oDate of Viva-voce|d2023-12-29 |
| 520 | __ | |aA remarkable amount of research has been proceeding to apply machine learning techniques to produce healthcare solutions due to availability, adaptability and advancement of cloud and web technologies. On the other hand, COVID19 diagnosis process in its current form is facing the problems of shortage of medical resources with high growth of confirmed cases that results in large waiting time for screening of COVID19 patients. Increase in diagnosis time enhances the chances of cross infection. Essential requirement to stop the outbreak is early diagnosis of COVID19 patients. Even though cases are under control, people living to remote places cannot get timely treatment due to unavailability of specialized experts. Machine learning based predictive models can come up with the solution to the issues of COVID19 diagnosis by assisting in initial screening as well as helping experts in decision making. Researchers used machine learning classifiers to predict COVID19 positivity. We also propose a framework to clas |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aModi, Kirit J|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/545472|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.