Prediction of Heart Disease using Data Mining and Machine Learning

• Published in: 2024 International Visualization, Informatics and Technology Conference (IVIT) pp. 115 - 121
• Main Authors: Waseemullah, Manzoor, Saba, Baig, Mirza Adnan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.08.2024
• Subjects: ANN; Classification; Data mining; Dataset; Diseases; Heart; Logistic regression; Monitoring; Pain; Prediction algorithms; Random Forest; Random forests; Support vector machines; SVM; Testing
• DOI: 10.1109/IVIT62102.2024.10692836

In this new generation, cardiac disease is expanding abruptly. The main reason for spreading this disease is the way of life of human beings, such as poor nutrition, absence of activities, medication, smoking, and so on. Today's heart disease is normally happening in individuals, and 85% of people die due to heart disease. There is an enormous amount of data created every single day in the discipline of medication, which is put away in a clinical data bank. This dataset consists of rough data that consists of conflicting and excess information. There is no question about the well-being care framework; they are extremely wealthy in keeping information and yet exceptionally poor in bringing information. Data mining techniques may help in separating significant information by using data mining procedures such as classification, regression method, clustering, and so forth. Later, when the information gathered in the dataset increases and becomes more complicated, data mining algorithms are utilized. The target of this paper is to investigate the different data mining procedures, specifically Random Forest, logistic regression, and SVM, by using an approved dataset for the prediction of heart disease, which includes various characteristics such as age, sexual orientation, chest pain, blood pressure, hyperglycemia, and so on.