PREDICTION OF CARDIOVASCULAR MALADIES THROUGH THE IMPLEMENTATION OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.3336Keywords:
Cardio Vascular Disease, Data Analytics, Deep Learning, Machine Learning Algorithms, Predictive SystemAbstract [English]
The heart stands as a cornerstone within the human body's intricate network of blood circulation. Recognized as a vital organ, its pivotal role in sustaining life cannot be overstated. Heart disease, a formidable health challenge, demands serious consideration due to its significant impact on individual well-being and public health condition with a significant chance of mortality or serious long-term effects. Nevertheless, there are no effective methods for finding hidden patterns and linkages in e-health data. In order to save lives, medical diagnosis is a challenging but essential task that must be completed promptly and accurately. Clinical testing is costly, so in order to save expenses, we necessitate a precise computer-based automated decision support system that is apt for our needs. It has been suggested that machine learning applied to health analytics will enable accurate analysis of patient data. The medical field does not engage in data mining. When data mining techniques are used to patient risk factor data sets, an intelligent model can be created in the medical area. Recent advancements in data utilization have significantly impacted the field of knowledge discovery in databases (KDD), particularly in the realm of disease diagnosis. This study investigates the application of deep learning and machine learning methodologies in this context. With the emergence of numerous data mining classifiers, there is a growing focus on enhancing the accuracy and efficacy of disease diagnosis. This paper presents a novel approach—a heart attack prediction system—that leverages deep learning techniques, specifically the Multi-Layer Perceptron (MLP). MLP stands out as a sophisticated classification method, harnessing the power of artificial neural networks with deep learning capabilities. To ensure robust and dependable outcomes, the proposed methodology integrates data mining principles with deep learning techniques.
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