Abstract
Abstract—Information decision support systems are becomingmore in use as we are living in the era of digital data andrise of artificial intelligence. Heart disease as one of the mostknown and dangerous is getting very important attention, thisattention is translated into digital and prediction system thatdetects the presence of disease according to the available dataand information. Such systems faced a lot of problems since thefirst rise, but now with the deveolopment of machine learnigfield we are using them in developing new models to detect thepresence of this disease, in addition to algorithms data is veryimportant which also form the heart of the predicton systems,as we know prediction algorithms take decisions and thesedecisions must be based on facts, and these facts are extractedfrom data, as a result data is the starting point of every system.In this paper we propose a Heart Disease Prediction Systemusing Machine Learning Algorithms, in terms of data we usedCleveland dataset, this dataset is normalized then divided intothree scnearios in terms of traning and testing respectively,80%-20%, 50%-50%, 30%-70%. In each case of dataset ifit is normalized or not we will have these three scenarios.We used three machine learning algorithms for every scenarioof the mentioned before which are SVM, SMO and MLP, inthese algorithms we’ve used two different kernels to test theresults upon that. These two types of simulation are added tothe collection of scenarios mentioned above to become as thefollowing we have at the main level two types normalized andunnormalized dataset, then for each one we have three typesaccording to the amount of training and testing dataset, thenfor each of these scenarios we have two scenarios according tothe type of kernel to become 30 scenarios in total, our proposedsystem have shown a dominance in terms of accuracy over theother previous works.
Highlights
The problem of heart disease is widely popular among all cultures, it is the leading cause of death for men, women, and people of most racial and ethnic groups in the world, according to CDC (Centers od Disease Control and Prediction) 1 person dies every 36 seconds in the united states from heart disease, 655000 Americans die from heart disease every year, it costs the United States about 219 billion dollars each year which includes the cost of health care service, medicines and lost productivity due to death
Datasets have a large number of features which is not good for analysis since a large number of features increase the complexity of the model and the model will focus on several dimensions of data while abstracting the dimensions into smaller numbers will help more coring into a successful result
What we have is an empty model of machine learning choose and a dataset, what happens in this layer is that we train our model using the processed dataset, in this case, we will fill our model with logical use cases and which will make it prepared to the layer
Summary
The problem of heart disease is widely popular among all cultures, it is the leading cause of death for men, women, and people of most racial and ethnic groups in the world, according to CDC (Centers od Disease Control and Prediction) 1 person dies every 36 seconds in the united states from heart disease, 655000 Americans die from heart disease every year, it costs the United States about 219 billion dollars each year which includes the cost of health care service, medicines and lost productivity due to death. Heart disease prediction systems are usually composed of 3 layers, dataset layer, feature-selection layer and classification layer. Many machine learning algorithms equips data decision systems with logical and typical behaviour depending on the dataset used. What we have is an empty model of machine learning choose and a dataset, what happens in this layer is that we train our model using the processed dataset, in this case, we will fill our model with logical use cases and which will make it prepared to the layer. We can test the model we train, so after dividing the dataset in the first stages into training and testing, we use the testing part of the dataset to test the algorithm
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