The Impactful Analysis of ML Based Technology in Forecasting the Specific Disorders in a Community

In the healthcare industry, systems increasingly rely on data mining techniques to diagnose and make decisions about diseases. Cardiovascular disease (CVD) prognosis and complication assessment based on risk levels is an area where these approaches have the greatest impact. To this end, fuzzy logic...

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Bibliographic Details
Published in2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 263 - 265
Main Authors Khanna, M.Rajesh, Sarkar, Rashel, Saxena, Arpna, Loganayagi, S, Dheidan, Ali Hasan, Muhsin, Omar
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.05.2024
Subjects
Accuracy
Adaptation models
ANFIS
Association rule
Cardio vascular
CVD
Fuzzy logic
Heart
Industries
machine learning
Medical services
Predictive models
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Summary:In the healthcare industry, systems increasingly rely on data mining techniques to diagnose and make decisions about diseases. Cardiovascular disease (CVD) prognosis and complication assessment based on risk levels is an area where these approaches have the greatest impact. To this end, fuzzy logic is used to develop an association rule that provides a framework for disease prediction. In addition, the Adaptive Neuro Fuzzy Inference System (ANFIS) is used to quantify the robustness in CVD prediction. In this study, the model was thoroughly tested to investigate the effect of different parameter values on its prediction performance. By adjusting these variables, the scientists sought to maximize the predictive power of the model, assuring its accuracy and reliability in describing heart conditions and the associated complications were examined. The cardiovascular prediction accuracy obtained in this study was remarkably high, reaching 97.39%. This high accuracy indicates the efficiency of the data mining methods used, as well as the robustness of the prediction model developed Such accurate predictions can greatly help healthcare providers to identify individuals who are at risk for heart disease and implement timely interventions to reduce these risks.
DOI:10.1109/ICACITE60783.2024.10616989