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              <text>A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field</text>
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              <text>Data Mining; Healthcare; Machine Learning classifiers; Prediction Analysis; Symptoms</text>
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              <text>In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than.  2024 IEEE.</text>
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              <text>Sharma K.; Madeswaran A.; Chandramauli A.; Narasimha V.; Jasim L.; Reeja Y.M.</text>
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              <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 968-972.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICACITE60783.2024.10616437" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACITE60783.2024.10616437&lt;/a&gt;
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              <text>ISBN: 979-835036016-5</text>
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              <text>Sharma K., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Madeswaran A., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur campus, India; Chandramauli A., Uttaranchal University, Uttaranchal Institute of Technology, Department of Civil Engineering, Dehradun, 248007, India; Narasimha V., Cmr College of Engineering &amp;amp; Technology, Department of Cse, India; Jasim L., The Islamic University, Najaf, Iraq; Reeja Y.M., Saveetha Institute of Medical and Technical Sciences, Saveetha University, Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India</text>
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