Forecast of Heart Sickness using Machine Learning

Heart problems are also known as cardiovascular disease, and the effect of heart disease and death rates have grown in recent decades as a result of numerous heart disease situations. Many risk factors are linked to heart disease, which necessitates taking the effort to develop reliable, accurate, and practical strategies for recognizing an initial discovery and achieving quick disease management. Heart problems are present at an early stage, indicating that the patient's health has to be improved. On the basis of supervised learning algorithms, this study paper gives numerous attributes related with heart disease and the model. Here, Machine Learning techniques are used to forecast the emergence of cardiac ailments at an earlier stage in order to fix the problem. A tendency is used to examine the parameters such as sex, age, and weight, as well as tests such as cholesterol, blood pressure, and diabetes, are used to predict outcomes. Many algorithms are employed to tackle this problem in machine learning. The logistic regression techniques are used to forecast the patient's heart disease in this paper. Patients' health issues are also recommended by logistic regression algorithms, as well as health advice to help them improve their health. The goal of this study is to predict the progression of cardiac disease in patients. The findings show that the logistic regression technique achieves the greatest accuracy score.