to download project abstract

Ultrasound insonation of lungs that are dense with extravascular lung
water (EVLW) produces characteristic reverberation artifacts termed B-lines.
The number of B-lines present demonstrates reasonable correlation to the
amount of EVLW. However, analysis of B-line artifacts generated by this
modality is semi-quantitative relying on visual interpretation, and as a result,
can be subject to inter-observer variability. The purpose of this study was to
translate the use of a novel, quantitative lung ultrasound surface wave
elastography technique (LUSWE) into the bedside assessment of pulmonary
edema in patients admitted with acute congestive heart failure. To prevent this
problem in One of the most interesting (or perhaps most profitable) time series
data using machine learning techniques. Hence, pulmonary disease prediction
has become an important research area. The aim is to predict machine learning
based techniques for pulmonary disease prediction results in best accuracy. The
analysis of dataset by supervised machine learning technique(SMLT) to capture
several information’s like, variable identification, uni-variate analysis, bi-variate
and multi-variate analysis, missing value treatments and analyze the data
validation, data cleaning/preparing and data visualization will be done on the
entire given dataset. To propose a machine learning-based method to accurately
predict the pulmonary diseaseIndex value by prediction results in the form of
pulmonary disease classification best accuracy from comparing supervise
classification machine learning algorithms.
Additionally, to compare and
discuss the performance of various machine learning algorithms from the given
dataset with evaluation classification report, identify the confusion matrix and
to categorizing data from priority and the result shows that the effectiveness of
the proposed machine learning algorithm technique can be compared with best
accuracy with precision, Recall and F1 Score.

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