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When it comes to mobility issues and heart disease, a machine learning computer can make critical predictions. The remainder of the body is the largest and most concentrated organ in the human body when compared to the heart. Predicting cardiac disease via data analysis is a critical medical endeavor. The medical business throughout the world recycles machine learning. When it comes to machine learning, whether a person has mobility abnormalities or heart ailments is a critical consideration. In medical facilities, data analysis aids in the prediction of more information and the prevention of certain diseases. The study paper's major objective is to forecast a patient's heart condition using a machine learning method such as a random forest, which is the most reliable. Every month, a huge amount of patient data is archived. The information that has been collected can be utilized to make predictions about what illnesses will arise in the future. Certain data mining and machine learning technologies are utilized to anticipate cardiac illness, such as artificial neural networks (ANN), decision trees, fuzzy logic, K-Nearest neighbors (KNN), naïve bays and vector support equipment, for example (SVM). The final goal of this research is to examine the best python learning-based logistic regression model. It is a machine learning model. The heart disease data sets were utilized by the UCI machine learning depot. |
