Heart Disease Detection by Machine Learning System

Abstract

Heart disease is a prevalent global health issue that impacts a substantial number of individuals worldwide. It is characterized by symptoms such as shortness of breath, muscle weakness, and swollen feet. However, the current diagnostic methods for heart disease have limitations in terms of accuracy and efficiency, making early detection challenging. Consequently, researchers are striving to develop an effective approach for early detection of heart disease. The lack of advanced medical equipment and qualified healthcare professionals further complicates the diagnosis and management of cardiac conditions., there have been approximately 26 million reported cases of heart disease, with an additional 3.6 million new cases identified annually. In the United States, a significant proportion of the population is affected by heart disease. Typically, doctors diagnose heart disease by considering the patient's medical history, conducting a physical examination, and assessing any concerning symptoms. However, this diagnostic method does not consistently provide accurate identification of individuals with heart disease. The importance of employing. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. “Patient 1,patient 2,patient 3 and patient 4.” Dyspnea can be described as a sensation of breathlessness and inadequate breathing, where one feels unable to take in enough air or breathe deeply. It involves the interplay of mechanoreceptors in the upper airways, lungs, and chest wall, along with peripheral receptors, chemoreceptors, and other sensory receptors. Edema refers to the accumulation of excessive fluid in the body tissues, leading to swelling. While edema can occur in any part of the body, it is more commonly observed in the lower extremities Ascites - The pathological buildup of fluid in the abdominal cavity is known as ascites. It is the most frequent cirrhosis consequence and happens in 50% of patients with decompensated cirrhosis within 10 years. Ascites formation marks the change from stressed to decompensated cirrhosis. Patent 1 is in rank 1 and patient 5 is ranked 5. In weighted table every value is equally split by 1,so that each value is equal. In the study, the researchers compared the sensitivity levels of two classifiers: the Relief FS method with a linear SVM classifier and the NB classifier with specific features from the LASSO FS algorithm. The findings revealed that the NB classifier, utilizing LASSO FS features, exhibited the highest performance in terms of sensitivity. Additionally, the Logistic Regression MCC classifier, employing the FCMIM FS method, achieved a classification accuracy of 91%.