In the relentless pursuit of early detection and accurate diagnosis of cardiovascular diseases (CVD), a groundbreaking framework has emerged, promising to revolutionize healthcare outcomes. Developed by Chenji Keerthipriya from the Department of Electronics and Communication Engineering at Gokula Krishna College of Engineering, Sullurpet, this innovative system leverages machine learning to detect and classify five major cardiovascular disorders: Heart Attack, Heart Failure, Heart Valve disease, pericardial disease, and vascular disease.
The urgency of this research cannot be overstated. Cardiovascular disease remains the leading cause of mortality worldwide, with cases on the rise. “Detecting CVD efficiently and accurately in large populations has become an urgent necessity,” Keerthipriya emphasizes. The proposed framework aims to address this critical need by providing a robust tool for early and precise identification of cardiovascular disorders.
Developed within the Matlab environment, the system undergoes comprehensive simulation to ensure its effectiveness. To evaluate its performance, Keerthipriya employs a dual approach: psycho-visual and parametric analysis. Psycho-visual analysis involves human experts visually assessing the system’s outputs, offering qualitative insights into its accuracy and reliability. Meanwhile, parametric analysis uses objective metrics to quantitatively measure the system’s efficiency in detecting and categorizing the cardiovascular conditions.
The implications of this research extend beyond the medical field, potentially impacting the energy sector as well. Early detection and accurate diagnosis of CVD can lead to timely medical interventions, reducing the burden on healthcare systems and improving patient outcomes. This, in turn, can enhance workforce productivity and reduce absenteeism, benefiting industries, including the energy sector, where physical labor and high-stress environments are common.
Keerthipriya’s research, published in the International Journal of Emerging Research in Engineering, Science, and Management (translated to English as ‘International Journal of Emerging Research in Engineering, Science, and Management’), represents a significant step forward in the fight against cardiovascular disease. By incorporating both subjective and objective evaluations, the study aims to develop a robust and efficient tool for cardiovascular disease screening, ultimately contributing to enhanced healthcare and reduced disease burden.
As we look to the future, this research holds promise for shaping developments in the field of cardiovascular health. The integration of machine learning and advanced analytical techniques could pave the way for more sophisticated and accurate diagnostic tools, improving patient care and outcomes. Moreover, the potential commercial impacts for the energy sector highlight the broader implications of this research, underscoring the importance of interdisciplinary collaboration in addressing global health challenges.