Clustering similar time series data for the prediction the patients with heart disease

Abstract

Developed intelligent technologies are become play a promising role in providing better decision-making and improving the medical services provided to the patients. A risk prediction task for short-term is big challenge task; however, it is a great importance for recommendation systems in health care field to provide patients with accurate and reliable recommendations. In this work, clustering method and least square support vector machine are used for prediction a short-term disease risk prediction. The clustering similar method is based on euclidean Distance which used to identify the similar sliding windows. The proposed model is trained by using the slide windows samples. Finally, the appropriate recommendations are generated for heart diseases patients who need to take a medical test or not for following day using least square support vector machine. A real dataset which collected from heart diseases patient is used for evaluation. The proposed method yields a good results related by the recommendations accuracy generated to chronicle heart patients and reduce the risk of incorrect recommendations.

Prediction of heart disease outcomes using machine learning classifier

“…Sex, kind of chest pain, resting blood pressure, cholesterol, maximum heart rate, exercise-induced angina, Slope, vessels colored by flourosopy, thalassemia, and old peak are the variables that are significant. The results finding shows an agreement with the previous studies that Machine learning were effective in predicting heart and circulatory disorders [15]- [26]. Table 3 compares the forward and backward methods of variable selection, and it can be seen that the forward technique is more efficient, it shows that the forward method has the suitable model with the accuracy of 88.6%, sensitivity of 91.4% and specificity of 85.6% and it's the suitable model that fits the data.…”

“…In each forward step, we add the one variable that provides the most improvement to the model. Backward elimination is nearly the converse of forward elimination; we begin with a model that includes every feasible variable and gradually remove the superfluous variables [25], [26].…”