PERFORMANCE ANALYSIS OF HEART DISEASE PREDICTION

Information Technology

Heart disease is a major cause of morbidity and mortality globally, and early detection is crucial for effective management. Models for machine learning have been created to aid in the prediction of heart disease, with LightGBM being one such model. This study aims to analyse the effectiveness of LightGBM in predicting heart disease. LightGBM was implemented using Python, and the model was trained using the drill set. The model's performance was evaluated using several metrics such as recall, accuracy, and precision F1 score, and region underneath the receiver operating characteristic (ROC) curve.This research shows that LightGBM is an effective model for predicting heart disease. Further studies could be conducted to assess the model's performance on larger datasets and to compare it to other performances machine learning mode. One of the most common most common diseases worldwide, and many people have died as a result of it. Diseases may have an impact on people both physically and emotionally, since getting and living with an illness can change a person's outlook on life. An illness that affects several areas of an organism yet is not caused by an instant exterior damage. Diseases are frequently defined as medical disorders characterised by distinct symptoms and indicators. The most lethal illnesses in humans are arteria coronary disease (blood flow blockage), cerebrovascular disease, and lower respiratory infections. Heart disease is the most unexpected and unpredictability. With machine learning, we can anticipate cardiac disease. To get high efficiency output, we employ Convolutional Neural Network approaches.

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