Diagnosis of Early Cardiac Disease by Applying Machine Learning Algorithms

In today's busy world, people are often overwhelmed, striving to meet their desires, and neglecting self-care. This leads to physical strain and emotional turmoil. As a consequence, heart diseases are very common, especially in urban areas where mental stress levels are high. Cardiovascular problems have now become a major cause of death in both men and women. The challenge lies in predicting heart diseases accurately within the medical field. Hence, there's an increasing need for developing a dependable system to predict the likelihood of developing heart diseases in the present day. Early prediction of heart diseases can save many lives. The objective of this research is to create a fast-operating and accurate system for foreseeing the possibility of heart diseases. Machine learning (ML) has demonstrated its value in making predictions and decisions from the extensive data produced by hospitals and the healthcare sector. Various combinations of options and classification methods are integrated into the proposed prediction model. Diverse machine learning techniques, such as decision trees, random forests, and logistic regression, were employed to assess prediction algorithms for heart diseases using a wide array of input factors. The proposed algorithm uses common medical terms like BMI, smoking, alcohol use, diabetes, and more to predict the likelihood of cardiovascular problems in a patient.