Detection of chronic diseases based on the principles of deep and machine learning.

continuing care is referred to as a chronic disease. The most widespread and expensive medical illnesses worldwide are chronic diseases. Chronic diseases can result in hospitalization, long-term impairment, worse quality of life, and even death. These conditions include cancer, diabetes, hypertension, stroke, heart disease, respiratory conditions, and kidney diseases. In reality, the greatest cause of mortality and disability worldwide is chronic illnesses. In this paper, we present deep-based and machine-based models to diagnose chronic diseases, this system includes several stages, namely the stage of data pre-processing and the stage of disease detection, which is carried out in two ways, the first depending on a deep Convolution Neural Network (CNN) and the second based on five machine learning algorithms: Stochastic Gradient Descent (SGD), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The proposed model works on three data sets, namely (Pima Indians Diabetes Dataset, Cardiovascular Disease dataset, and UCI Heart Disease Data) to classify heart, diabetes, and kidney diseases. The experimental results proved the capability of the suggested system to classify the aforementioned diseases with an ideal accuracy of 100% using the CNN in the first model, and an accuracy of 94% in the second model using the SGD and LR algorithms. [ABSTRACT FROM AUTHOR]