Abstract
Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models.
Highlights
We propose a system based on filling missing data algorithms and machine learning models to diagnose cardiovascular diseases
To remedy the problem of missing values, we proposed to fill up missing cells with different techniques, such as mean value, K Nearest Neighbor (KNN), Random Forest (RF), and Multiple Imputations by Chained Equations (MICE)
Results and performances of classification We evaluated the performance of our model using five specific performance measures namely: accuracy, specificity, recall, and F-measure
Summary
According to the World Health Organization, the number of deaths will add up by 24.5 million in 2030 [1], because of the growth of cardiovascular risk factors such as high blood pressure, diabetes, obesity, and smoking.In several cases, saving the patient’s life rely crucially on the time before being seen by a doctor and finding the required hospitalization, so giving the physicians perpetual update concerning their patient’s health conditions will considerably scale back the number of deaths.Many factors could cause cardiopathy such as the dynamic changes in lifestyle, smoking, food habits, physical activity, obesity, diabetes, and biochemical factors like blood pressure or glycaemia [2, 3], whereas the common symptoms of cardiovascular diseases can be a pain in arms and chest [4]. According to the World Health Organization, the number of deaths will add up by 24.5 million in 2030 [1], because of the growth of cardiovascular risk factors such as high blood pressure, diabetes, obesity, and smoking. In a Medical diagnosis, the doctor tries to distinguish the heart defect by analyzing the values of a variety of characteristics This task is primarily based on a range of traditional ways like ECG, occultation, taking measures of blood pressure, blood sugar, and cholesterol. Those techniques are expensive and time consuming, and could lead to human errors [5]. A variety of machine learning techniques are used in various studies to aid in the diagnosis of heart diseases [6, 7] , and cardiovascular disease classification [8]
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