Multivariate Radial Interpolation Based Ruzicka Indexive Censored Regression for Early Prediction of Cardiovascular Disease

Cardiovascular diseases(CVDs) are widespread
disease affecting heart as well as blood vessels, resulting in
various complications like heart attacks, strokes, and peripheral
artery disease. These diseases are extremely diverse as well as
lead to dissimilar kinds of difficulty to minimize quality of life as
well as even decease. Early detection and effective management
of risk factors play pivotal roles for preventing complications
and enhancing outcomes for individuals affected with
cardiovascular disease. Many machine learning techniques have
designed to solve heart problems, but higher prediction
accuracy level with minimal time and space complexity
remained challenging issues. To enhance cardiovascular disease
forecast accuracy, a novel method called Multivariate Radial
Interpolation based Ruzicka indexive censored regression (MRIRICR)
is introduced. The proposed MRI-RICR method utilizes
the Multivariate radial basis interpolation method to estimate
missing information points depend on recognized data points.
Chauvinist’s criterion is employed for identifying and removing
outlier data from the dataset. MRI-RICR method utilizes the
Ruzicka indexive censored regression method to select relevant
features. This process improves the accuracy of cardiovascular
disease prediction through lesser time utilization. Experimental
assessment considers cardiovascular disease prediction
accuracy, precision, cardiovascular disease prediction time and
space complexity. The analyzed outcomes show which MRIRICR
method attain effective performance results, containing
superior cardiovascular disease prediction accuracy, precision,
through reduced time utilization ,space complexity than the
existing techniques.