Heart And Diabetes Disease Prediction Using Machine Learning
The Diabetes disease Heart disease (HD) has been considered as one of the complex and life deadliest human diseases in the world. In Heart disease, usually the heart is unable to push the required amount of blood to other parts of the body to fulfill the normal functionalities of the body, and due to this, ultimately the heart failure occurs. The rate of heart disease in the United States is very high. The symptoms of heart disease include shortness of breath, weakness of physical body, swollen feet, and fatigue with related signs, for example, elevated jugular venous pressure and peripheral edema caused by functional cardiac or noncardiac abnormalities. The investigation techniques in early stages used to identify heart disease were complicated, and its resulting complexity is one of the major reasons that affect the standard of life. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of diagnostic apparatus and shortage of physicians and others resources which affect proper prediction and treatment of heart patients. The accurate and proper diagnosis of the heart disease risk in patients is necessary for reducing their associated risks of severe heart issues and improving security of heart. The European Society of Cardiology (ESC) reported that 26 million adults worldwide were diagnosed with heart disease and 3.6 million were diagnosed every year. Approximately 50% of heart disease people suffering from HD die within initial 1-2 years, and concerned costs of heart disease management are approximately 3% of health-care financial budget.
Diabetes is a common chronic disease and poses a great threat to human health. The characteristic of diabetes is that the blood glucose is higher than the normal level, which is caused by defective insulin secretion or its impaired biological effects, or both . Diabetes can lead to chronic damage and dysfunction of various tissues, especially eyes, kidneys, heart, blood vessels and nerves . Diabetes can be divided into two categories, type 1 diabetes (T1D) and type 2 diabetes (T2D). Patients with type 1 diabetes are normally younger, mostly less than 30 years old. The typical clinical symptoms are increased thirst and frequent urination, high blood glucose levels . This type of diabetes cannot be cured effectively with oral medications alone and the patients are required insulin therapy. Type 2 diabetes occurs more commonly in middle-aged and elderly people, which is often associated with the occurrence of obesity, hypertension, dyslipidemia, arteriosclerosis, and other diseases .
In order to resolve these complexities in invasive-based diagnosing of heart disease, a noninvasive medical decision support system based on machine learning predictive models such as support vector machine (SVM), decision tree (DT),Naive Bayes (NB) and rough set [9, 10] has been developed by various researchers and widely used for heart disease diagnosis, and due to these machine-learning-based expert medical decision system, the ratio of heart disease death decreased. Heart disease diagnosis through the machine-learning-based system has been reported in various research studies. The classification performance of different machine learning algorithms on Cleveland heart disease dataset has been reported in the literature review. Cleveland heart disease dataset is online available on the University of California Irvine (UCI) data mining repository which was used by various researchers. This is the dataset that has been used by various researchers for investigation of different classification issues related to the heart diseases through different machine learning classification algorithms.