Intelligent Disease Diagnosis: A Multi-Disease Prediction Approach Using Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET251235Keywords:
Data-Driven, K-Nearest Neighbour (KNN), Healthcare, Random Forest, Disease Prediction, Support Vector Machine (SVM), Machine LearningAbstract
Diseases prediction, which seeks to pinpoint persons at risk of developing particular illnesses, is an essential aspect of healthcare. Recently, machine learning algorithms have shown to be excellent instruments in sickness prediction, owing to their exceptional capacity to analyse extensive datasets for intricate patterns. The advancement of Machine Learning (ML) in modern healthcare has generated novel potential for the detection and treatment of chronic diseases. This study provides a comprehensive Multiple Disease Prediction System that accurately predicts diabetes, cancer, and heart disease via machine learning techniques. The system aims to analyse complex medical information to identify patterns and risk factors associated with these diseases. The system employs cardiovascular data analysis and logistic regression to identify heart illness and provide a probabilistic assessment of cardiac health. Convolutional Neural Networks, which analyse medical imaging to identify malignancies with high accuracy, are used to enhance cancer diagnosis. Support Vector Machines are used to forecast diabetes by considering many metabolic and genetic variables for assessment. The objective of this initiative is to assist individuals in identifying their health concerns just by their symptoms and precise vital signs. The suggested methodology demonstrates that the system is exceptionally precise and efficient, providing a significant decision support tool for physicians and health-conscious consumers.
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References
C. T. Kor et al., "Explainable machine learning model for predicting first-time acute exacerbation in patients with chronic obstructive pulmonary disease", Journal of Personalized Medicine, vol. 12, no. 2, pp. 228, 2022
Y. Perwej, Dr. Faiyaz Ahamad, Dr. Mohammad Zunnun Khan, N. Akhtar, “An Empirical Study on the Current State of Internet of Multimedia Things (IoMT)”, International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), ISSN (Online) 2394-2320, Volume 8, Issue 3, Pages 25 - 42, 2021, doi: 10.1617/vol8/iss3/pid85026
Littell, C.L., "Innovation in medical technology: Reading the indicators", Health Affairs, Vol. 13, No. 3, (1994), 226-235. https://doi.org/10.1377/hlthaff.13.3.226
Y. Perwej, Mohammed Y. Alzahrani, F. A. Mazarbhuiya, Md. Husamuddin, “The State-of-the-Art Cardiac Illness Prediction Using Novel Data Mining Technique”, International Journal of Engineering Sciences & Research Technology (IJESRT), ISSN: 2277-9655, Volume 7, Issue 2, Pages 725-739, 2018, DOI: 10.5281/zenodo.1184068
Mobeen, A., Shafiq, M., Aziz, M.H. and Mohsin, M.J., "Impact of workflow interruptions on baseline activities of the doctors working in the emergency department", BMJ Open Quality, Vol. 11, No. 3, 2022
Ahmed, S., Szabo, S. and Nilsen, K., "Catastrophic healthcare expenditure and impoverishment in tropical deltas: Evidence from the mekong delta region", International Journal for Equity in Health, Vol. 17, No. 1, 1-13, 2018
Roberts, M.A. and Abery, B.H., "A person-centered approach to home and community-based services outcome measurement", Frontiers in rehabilitation Sci., Vol. 4, 2023
Y. Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
M. Chen, Y. Hao, K. Hwang, L. Wang and L. Wang, "Disease prediction by machine learning over big data from healthcare communities", Ieee Access, vol. 5, pp. 8869-8879, 2017
M. A. E. Van Stiphout, J. Marinus, J. J. Van Hilten, F. Lobbezoo, and C. De Baat, “Oral health of Parkinson’s disease patients: a case-control study,” Parkinson’s Disease, vol. 2018, Article ID 9315285, 8 pages, 2018
Y. Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), which is published by Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
Y. Jian, M. Pasquier, A. Sagahyroon, and F. Aloul, “A Machine Learning Approach to Predicting Diabetes Complications,” Healthcare, vol. 9, no. 12, Art. no. 12, Dec. 2021, doi: 10.3390/healthcare9121712.
C. Zhang et al., "Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease", Contrast Media & Molecular Imaging, 2022
Y. Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), SCOPUS, IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501
Cao, J., Wang, M., Li, Y. and Zhang, Q., "Improved support vector machine classification algorithm based on adaptive feature weight updating in the hadoop cluster environment", PloS One, Vol. 14, No. 4, 2019
Y. Perwej, S. A. Hann, N.t Akhtar, “The State-of-the-Art Handwritten Recognition of Arabic Script Using Simplified Fuzzy ARTMAP and Hidden Markov Models”, International Journal of Computer Science and Telecommunications (IJCST), Sysbase Solution (Ltd), UK, London, ISSN 2047-3338, Volume, Issue 8, Pages, 26 - 32, 2014
Y. Wang et al., "Using Machine Learning Models for Early Diagnosis of Heart Disease Based on Imbalanced Data", IEEE Access, vol. 10, pp. 57276-57287, 2022
Maurano, M., Humbert, R., Rynes, E., Thurman, R., Haugen, E., Wang, H., Reynolds, A., Sandstrom, R., Qu, H., Brody, J., 2012. Systematic Lo calization of Common Disease-Associated Variation in Regulatory DNA. Science, 337, pp. 1190- 1195. https://doi.org/10.1126/science.1222794.
Kumar, A., 2021. Disease Prediction and Doctor Recommenda tion System using Machine Learning Approaches. International Jour nal for Research in Applied Science and Eng. Tech.. DOI: 10.22214/IJRASET.2021.36234.
Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
Patil, K., Pawar, S., Sandhyan, P., Kundale, J., 2022. Multiple Disease Prognostication Based On Symptoms Using Machine Learning Techniques. ITM Web of Conferences. https://doi.org/10.1051/itmconf/20224403008.
T. Takura, K. H. Goto, and A. Honda, “Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases,” BMC Medicine, vol. 19, no. 1, pp. 1–16, 2021.
N. Akhtar, Saima Rahman, Halima Sadia, Y. Perwej, “A Holistic Analysis of Medical Internet of Things (MIoT)”, Journal of Information and Computational Science (JOICS), ISSN: 1548 - 7741, Volume 11, Issue 4, Pages 209 - 222, 2021, DOI: 10.12733/JICS.2021/V11I3.535569.31023
L. Beretta and A. Santaniello, “Nearest neighbor imputation algorithms: a critical evaluation,” BMC Medical Informatics and Dec.Making, vol. 16, no. 3, pp. 197–208, 2016.
T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system", Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 785-794, 2016
Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, N. Akhtar, Y. Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
G. Litjens et al., "Deep Learning for Medical Image Analysis: A Survey", ACM Computing Surveys, vol. 56, no. 2, pp. 1-41, 2023
Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, N. Akhtar, Y. Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, for published in the International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
N.Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
Y. Jian, M. Pasquier, A. Sagahyroon, and F. Aloul, “A Machine Learning Approach to Predicting Diabetes Complications,” Healthcare, vol. 9, no. 12, Art. no. 12, Dec. 2021, doi: 10.3390/healthcare9121712
D. J. Park, M. W. Park, H. Lee, Y.-J. Kim, Y. Kim, and Y. H. Park, “Development of machine learning model for diagnostic disease prediction based on laboratory tests,” Scientific Reports, vol. 11, no. 1, pp. 1–11, 2021
N. Akhtar, H. Pant, Apoorva Dwivedi, Vivek Jain, Y. Perwej, “A Breast Cancer Diagnosis Framework Based on Machine Learning”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Volume 10, Issue 3, Pages 118-132, May-June-2023, DOI: 10.32628/IJSRSET2310375
Shweta Pandey, Rohit Agarwal, Sachin Bhardwaj, Sanjay Kumar Singh, Y. Perwej, Niraj Kumar Singh, “A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner” , the International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Volume 9, Issue 1, Pages 206-227, 2023, DOI: 10.32628/CSEIT2390147
E Benmalek, J Elmhamdi and A. Jilbab, "UPDRS tracking using linear regression and neural network for Parkinson’s disease prediction", International Journal Of Emerging Trends and Technology In Com. Science, vol. 4, pp. 189-193, 2015
Y. Perwej, Mohammed Y. Alzahrani, F. A. Mazarbhuiya, Md. Husamuddin, “The State-of-the-Art Cardiac Illness Prediction Using Novel Data Mining Technique”, International Journal of Engineering Sciences & Research Technology (IJESRT), ISSN: 2277-9655, Volume 7, Issue 2, Pages 725-739, 2018, DOI: 10.5281/zenodo.1184068
C.R. Pereira, D.R. Pereira, F.A. Da Silva, C. Hook, S.A. Weber, L.A. Pereira, et al., "A step towards the automated diagnosis of Parkinson's disease: Analyzing handwriting movements", Computer-Based Medical Systems (CBMS) 2015 IEEE 28th International Symposium on, pp. 171-176, 2015
W. Wang, J. Lee, F. Harrou and Y. Sun, "Early detection of Parkinson’s disease using deep learning and machine learning", IEEE Access, vol. 8, pp. 147635-147646, 2020
M. Bogart et al., "Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model", International Journal of Chronic Obstructive Pulmonary Disease, pp. 735-747, 2022
N. Akhtar, Nazia Tabassum, Dr. Asif Perwej, Y. Perwej,“ Data Analytics and Visualization Using Tableau Utilitarian for COVID-19 (Coronavirus)”, Global Journal of Engineering and Technology Advances (GJETA), Volume 3, Issue 2, Pages 28-50, 2020, DOI: 10.30574/gjeta.2020.3.2.0029
JD Huang et al., "Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review", Sensors, vol. 22, no. 20, pp. 1-28, 2022
Dixit, S., & Jangid, J. (2024). Asynchronous SCIM profile for security event tokens. Journal of Computational Analysis and Applications, 33(6), 1357–1371. https://eudoxuspress.com/index.php/pub/article/view/1935
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