CLINICAL PREDICTIVE MODEL USING MACHINE LEARNING - ZEESHAN, MOHAMMAD;Kumar, Ashutosh;Shekhar, Ravi
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In the healthcare industry, big data analytics is extremely important, evidently since the industry itself is home to a vast sea of datasets. Analytics is used to examine these datasets and uncover hidden information and trends in order to extract knowledge and anticipate outcomes. The current existing approaches lack considerable categorization and prediction accuracy since the fetching of structured healthcare and clinical data is time-consuming and accurate prediction of diseases using real-time reports is a tough and computationally intensive task. Therefore, understanding motives behind…mehr

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Produktbeschreibung
In the healthcare industry, big data analytics is extremely important, evidently since the industry itself is home to a vast sea of datasets. Analytics is used to examine these datasets and uncover hidden information and trends in order to extract knowledge and anticipate outcomes. The current existing approaches lack considerable categorization and prediction accuracy since the fetching of structured healthcare and clinical data is time-consuming and accurate prediction of diseases using real-time reports is a tough and computationally intensive task. Therefore, understanding motives behind machine learning approaches in healthcare are essential, since precision and accuracy are often critical in healthcare problems. The aims is to build a generalized clinical machine learning predictive model using supervised classification algorithms, in-order to predict various common yet severe health diseases through a binary output.
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Autorenporträt
The author holds a PhD degree in 'Computational & Theoretical Chemistry' from prestigious IIT Madras under the tutelage of some of the best theoreticians in the country. His research interests lie in the field of 'Stochastic Biochemical Networks' ranging from enzyme kinetics, nanocatalysis to gene expression with extensive use of Python, MATLAB.