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• W3025800360 abstract "This paper aims to improve the quality of the patient's life and provide them with the lifestyle they need. And we have the intention to obtain this by creating a mobile application that analyzes the patient's data such as diabetes, blood pressure, and kidney. Then, implement the system to diagnose patients of chronic diseases using machine learning techniques such as classification. It's hard for the patients of chronic diseases to record their measurements on a paper every time they measure either the blood pressure or sugar level or any other disease that needs periodic measurements. The paper might be lost, and this can lead the doctor not fully to understand the case. So, the application is going to record measurements in the database. Also, it's difficult for patients to decide what to eat or how many times they should exercise according to their situation. Our idea is to recommend a lifestyle for the patient and make the doctor participate in it by writing notes. In this paper, machine learning classifiers were used to predict whether the person is prone to some chronic diseases. Blood pressure, diabetes and kidney are considered in this work. Orange3 from Anaconda-Navigator is the data mining tool used to test some machine learning algorithms. Blood pressure is the amount of force that blood exerts on the walls of the arteries as it flows through them. When this pressure reaches high levels, it can lead to serious health problems. For hypertension, Tree algorithm has shown 100% accuracy, which was the best one. Chronic Kidney Disease (CKD) is a significant public health concern with rising prevalence. With a set of considered attributes such as specific gravity, albumin, serum creatinine, hemoglobin, packed cell volume and hypertension used to predict if the person has Kidney disease or not. For kidney, Random Forest algorithm has shown 100% accuracy, which was the best one among other algorithms tested. Diabetes is a chronic disease when it cannot the pancreas to produce insulin, or when the body cannot use the insulin the pancreas produced. We considered attributes such as pregnancies, glucose, blood pressure, skin thickness, insulin, diabetes pedigree function, age and BMI of a person to diagnose whether a patient has diabetes based on specific diagnostic measurements or not. For diabetes, neural networks have shown the best accuracy. It was 76.3%." @default.
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• W3025800360 date "2020-04-17" @default.
• W3025800360 modified "2023-09-26" @default.
• W3025800360 title "Chronic Diseases System Based on Machine Learning Techniques" @default.
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• W3025800360 doi "https://doi.org/10.18517/ijods.1.1.18-36.2020" @default.
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