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Disease Prediction Using Machine Learning (ML)

Harshvardhan ., Manish Kumar Rai, Pradyumn Kumar, Silki Kharaliya

Abstract


A technique called Machine Learning Disease Prediction uses symptoms reported by users or patients to forecast disease. The user-provided symptoms are entered into the system, and it outputs the disease probability. In disease forecasting, various popular supervised machine learning methods are known to be utilized. These algorithm estimates the likelihood of a disease occurrence. Precise analysis of medical information will support timely disease detection and management of patients based on their biological status and medical status growth. The precise analysis of medical information provided by Machine Learning Disease Prediction greatly aids in the early detection of diseases. Timely detection is paramount in ensuring that patients receive the necessary medical attention promptly, potentially preventing the disease from progressing to a more advanced stage.

Keywords


Disease prediction, symptoms, machine learning, patient care, diabetes.

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References


B. S. Kim, M. Ahn, W.-W. Cho, G Gao, J. Jang, and D.-W. Cho, “Engineering of diseased human skin equivalent using 3D cell printing for representing pathophysiological hallmarks of type 2 diabetes in vitro,” Biomaterials, vol. 272, Article ID 120776, 2021.

Y. Khourdifi, M. Bahaj, Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization, Int. J. Intell. Eng. Syst. 12(1), 242 (2019)

S. Mohan, C. Thirumalai, G. Srivastava, Effective heart disease prediction using hybrid machine learning techniques, IEEE Access 7, 81542 (2019)

R. Katarya and P. Srinivas, “Predicting heart disease at early stages using machine learning: A survey,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 302–305.

A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, “Prediction of heart disease using machine learning,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1275–1278.

A.S. Monto, S. Gravenstein, M. Elliott, M. Colopy, J. Schweinle, Clinical signs and symptoms predicting influenza infection, Archives of internal medicine 160(21), 3243 (2000)

D. Dahiwade, G. Patle, and E. Meshram, “Designing disease prediction model using machine learning approach,” Proceedings of the 3rd International Conference on Computing Methodologies and Communication, ICCMC 2019, no. Iccmc, pp. 1211–1215, 2019.

F. Q. Yuan, “Critical issues of applying machine learning to condition monitoring for failure diagnosis,” in 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2016, pp. 1903–1907

S. Jadhav, R. Kasar, N. Lade, M. Patil, and S. Kolte, “Disease Prediction by Machine Learning from Healthcare Communities,” International Journal of Scientific Research in Science and Technology, pp. 29–35, 2019.

R. Saravanan and P. Sujatha, “A state of art techniques on machine learning algorithms: A perspective of supervised learning approaches in data classification,” in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018, pp. 945– 949.

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.

T. Karayılan, O. Kılı¸c, in ¨ 2017 International Conference on Computer Science and Engineering (UBMK) (IEEE, 2017), pp. 719–723

B. Nithya and Dr. V. Ilango,” Predictive Analytics in Health Care Using Machine Learning Tools and Techniques”, International Conference on Intelligent Computing and Control Systems, 978-1-5386-2745-7,2017.

S. Shafi and G. A. Ansari, “Early prediction of diabetes disease & classification of algorithms using machine learning approach,” in Proceedings of the International Conference on Smart Data Intelligence, (ICSMDI 2021), Trichy, Tamil Nadu, April 2021.

H. M. D. Kabir, M. Abdar, A. Khosravi et al., “Spinalnet: deep neural network with gradual input,” IEEE Transactions on Artificial Intelligence, pp. 1–13, 2022.

Mani Butwall and Shraddha Kumar,” A Data Mining Approach for the Diagnosis of Diabetes Mellitus using Random Forest Classifier”, International Journal of Computer Applications, Volume 120 - Number 8,2015.

K. Rajesh and V. Sangeetha, “Application of Data Mining Methods and Techniques for Diabetes Diagnosis”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, September 2012.




DOI: https://doi.org/10.37591/rrjomst.v12i2.3235

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