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- Vol. 4 Issue 2
- Article
Identifying Multiple Disease Using Machine Learning
Authors
1 Assistant Professor, Dept. of Computer Science and Business System, Bannari Amman Institute of Technology, TN, India. 23Dept. of Computer Science and Business System, Bannari Amman Institute of Technology, TN, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 4, Issue 2 (2023)
- Published Online
- March-April 2023
- Pages
- 119-126
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
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IEEE ,3rd International Conference on Computing Methodologies and Communication (ICCMC).
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on Electrical and Electronics Engineering (ICE3).
3. A.Sivasangari, Baddigam Jaya Krishna Reddy,Annamareddy Kiran, P.Ajitha,” Diagnosis of Liver Disease using Machine Learning
Models” 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
4. Khan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. “machine Learning for Multigrade Brain Tumor
Classification in Smart Healthcare Systems: A Prospective Survey” IEEE Transactions on Neural Networks and Learning Systems (
Volume: 32, Issue: 2, Feb. 2021).
5. Khan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. “machine Learning for Multigrade Brain Tumor
Classification in Smart Healthcare Systems: A Prospective Survey” IEEE Transactions on Neural Networks and Learning Systems (
Volume: 32, Issue: 2, Feb. 2021).
6. G. Hemanth, M. Janardhan and L. Sujihelen, “Design and Implementing Brain Tumor Detection Using Machine Learning Approach”,
3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019.
7. Cao, C., Liu, F., Tan, H., Song, D., Shu, W. et al. (2018). Deep learning and its applications in biomedicine.Genomics, Proteomics &
Bioinformatics, 16(1), 17–32. DOI 10.1016/j.gpb.2017.07.003
8. Dr. Ankita Karale, Uday Talpade, Sahil Nikumbh, Laxman Wadekar , Parikshit Angre, “Multiple Disease Detection Using Machine
Learning: A Survey ”, International Journal for Research in Applied Science & Engineering Technology,June 2022,ISSN: 2321-9653
9. Ankush Singh, Ashish Yadav, Saloni Shah ,Prof. Renuka Nagpure, “Multiple Disease Prediction System”,International Research Journal
of Engineering and Technology, Mar 2022 , ISSN: 2395-0056
10. Raihan M, Mondal S, More A, Sagor M O F, Sikder G, Majumder M A & Ghosh K (2016,
11. December). Smartphone based ischemic heart disease (heart attack) risk prediction using clinical data and data mining approaches, a
prototype design. In 2016 19th International Conference on Computerand Information Technology (ICCIT) (pp. 299-303). IEEE.
12. Aishwarya Mujumdar, Dr. Vaidehi V, “Diabetes Prediction using Machine Learning Algorithms”,INTERNATIONAL CONFERENCE
ON RECENT TRENDS IN ADVANCED COMPUTING 2019,
13. ICRTAC 2019
14. Gauri D. Kalyankar, Shivananda R. Poojara and Nagaraj V. Dharwadkar,” Predictive Analysis of Diabetic Patient Data Using Machine
Learning and Hadoop”, International Conference On I-SMAC,978-1-5090-3243-3,2017
IEEE ,3rd International Conference on Computing Methodologies and Communication (ICCMC).
2. Archana Singh ,Rakesh Kumar, “Heart Disease Prediction Using Machine Learning Algorithms”, 2020 IEEE, International Conference
on Electrical and Electronics Engineering (ICE3).
3. A.Sivasangari, Baddigam Jaya Krishna Reddy,Annamareddy Kiran, P.Ajitha,” Diagnosis of Liver Disease using Machine Learning
Models” 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
4. Khan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. “machine Learning for Multigrade Brain Tumor
Classification in Smart Healthcare Systems: A Prospective Survey” IEEE Transactions on Neural Networks and Learning Systems (
Volume: 32, Issue: 2, Feb. 2021).
5. Khan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. “machine Learning for Multigrade Brain Tumor
Classification in Smart Healthcare Systems: A Prospective Survey” IEEE Transactions on Neural Networks and Learning Systems (
Volume: 32, Issue: 2, Feb. 2021).
6. G. Hemanth, M. Janardhan and L. Sujihelen, “Design and Implementing Brain Tumor Detection Using Machine Learning Approach”,
3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019.
7. Cao, C., Liu, F., Tan, H., Song, D., Shu, W. et al. (2018). Deep learning and its applications in biomedicine.Genomics, Proteomics &
Bioinformatics, 16(1), 17–32. DOI 10.1016/j.gpb.2017.07.003
8. Dr. Ankita Karale, Uday Talpade, Sahil Nikumbh, Laxman Wadekar , Parikshit Angre, “Multiple Disease Detection Using Machine
Learning: A Survey ”, International Journal for Research in Applied Science & Engineering Technology,June 2022,ISSN: 2321-9653
9. Ankush Singh, Ashish Yadav, Saloni Shah ,Prof. Renuka Nagpure, “Multiple Disease Prediction System”,International Research Journal
of Engineering and Technology, Mar 2022 , ISSN: 2395-0056
10. Raihan M, Mondal S, More A, Sagor M O F, Sikder G, Majumder M A & Ghosh K (2016,
11. December). Smartphone based ischemic heart disease (heart attack) risk prediction using clinical data and data mining approaches, a
prototype design. In 2016 19th International Conference on Computerand Information Technology (ICCIT) (pp. 299-303). IEEE.
12. Aishwarya Mujumdar, Dr. Vaidehi V, “Diabetes Prediction using Machine Learning Algorithms”,INTERNATIONAL CONFERENCE
ON RECENT TRENDS IN ADVANCED COMPUTING 2019,
13. ICRTAC 2019
14. Gauri D. Kalyankar, Shivananda R. Poojara and Nagaraj V. Dharwadkar,” Predictive Analysis of Diabetic Patient Data Using Machine
Learning and Hadoop”, International Conference On I-SMAC,978-1-5090-3243-3,2017
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