Heart disease is the primary cause of death in the past decade. At least one person dies in every minute due to this heart disease. Researchers have come up with methods of predicting the likelihood of one having a heart disease and means of preventing such occurrences. A significant way used is UCI machine learning repository (Masethe et al. 225). This technique gets data through data mining. There are several data mining techniques. Data mining is where researchers acquire data from available sources to help healthcare practitioners in diagnosing of heart disease (Wu 101). This method majorly reduces the number of deaths that occur per year due to heart disease by prediction method. This is a quick and efficient detection method.
This problem is an essential issue of discussion since very many people die due to this heart disease. It has been researched that most people who lose their lives due to chronic illnesses are people who are wealthy due to the kind of food they eat and their stress levels. In the business perspective, when we lose such individuals who grow our economy through their businesses, the economy goes down. It is therefore essential to discuss this topic to reduce such deaths in the community.
The primary technique used in data mining is the decision tree method where researchers compare different algorithm to seek better performance in the diagnosis of heart disease (Patel et al. 132). The dataset chosen is one that has various attributes that are used to predict on the possibility of heart disease (Rokach et al. 78). These qualities are age, sex, cp (chest pains), trestbps (resting blood pressure), chol (cholesterol level), FBS (fasting blood sugar), old peak, and the final rate of the likelihood of heart disease. This method does not assume previous knowledge of patients records. This process is efficient since the data is accessible to filter and understand. Algorithms such as j48, logic model tree algorithms and random forest decision tree algorithm are used for comparison. These algorithms are then tested to determine which combination is to bring out the best results (Patel et al. 132) . The evaluation will provide the best performance in diagnosing heart disease patients.
In conclusion, heart disease prediction is essential to reduce the number of deaths in the world. The most popular method used in prediction process is the decision tree method where researcher compares different algorithms to determine the best performance in diagnosis of heart disease. This technique is a very accurate and efficient method of decision making.
Patel, Jaymin, Dr TejalUpadhyay, and Samir Patel. "Heart Disease Prediction Using Machine learning and Data Mining Technique." Heart Disease 7.1 (2015).Wu, Xindong, et al. "Data mining with big data." IEEE transactions on knowledge and data engineering 26.1 (2014): 97-107.
Rokach, Lior, and Oded Maimon. Data mining with decision trees: theory and applications. World scientific, 2014.Masethe, Hlaudi Daniel, and Mosima Anna Masethe. "Prediction of heart disease using classification algorithms." Proceedings of the world Congress on Engineering and computer Science. Vol. 2. 2014.
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