[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127652-en":3,"doc-seo-127652-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127652,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Heart Disease Detection by Machine Learning System - Abstract","Heart disease is a major global health challenge, with symptoms such as shortness of breath, muscle weakness, and swollen feet. Existing diagnostic approaches often face limits in accuracy and efficiency, hindering early detection. This work focuses on developing an effective, noninvasive diagnostic method using machine learning, supported by dataset preparation, data preprocessing, and feature engineering to improve predictive performance. Reported results highlight the effectiveness of particular classifiers and feature-selection strategies.","Heart Disease Detection by Machine Learning System  \nDr. R. Sundar1, Dr. K. Maithili2, T. Raghavendra Gupta3, P L Srinivasa Murthy4, G. Nagarjuna Rao5  \n1Assistant Professor,Computer Science and Engineering,Madanapalle Institute of Technology & Science Post Box No: 14, Kadiri Road Angallu  \n(V), Madanapalle-517325Annamayya District, Andhra Pradesh, India.  \n[Mail :drsundarr@mits.ac.in](Mail :drsundarr@mits.ac.in)  \n2Associate Professor Department ofCSE KG REDDY COLLEGE OF ENGINEERING &TECHNOLOGY Moinabad, Hyderabad, [Telangana-501504 Mail : drmaithili@kgr.ac.in](Telangana-501504 Mail : drmaithili@kgr.ac.in)  \n3Associate Professor Department of computer science and Engineering Hyderabad Institute of Technologgy And Management Hyderabad, [Telangana Mail : raghu.ht@gmail.com](Telangana Mail : raghu.ht@gmail.com)  \n4 Professor, Department of Computer science and Engineering, [IARE Mail : plsrinivasamurthy@iare.ac.in](IARE Mail : plsrinivasamurthy@iare.ac.in)  \n5Assistant Professor Department ofCSE MLR Institute of Technology, Dundigal, Hyderabad  \n[Mail : nagarjunarao.gudelli@gmail.com](Mail : nagarjunarao.gudelli@gmail.com)  \nAbstract: 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","cbCaibuTdlXycyZL","https://ap.wps.com/l/cbCaibuTdlXycyZL","pdf",312982,2,1,"English","en",105,"# Introduction\n## Problem Background and Need for ML\n## Data Preparation and Preprocessing\n## Feature Engineering and Feature Selection\n## Classifier Comparison and Results","[{\"question\":\"Why is early heart disease detection difficult with current diagnostic methods?\",\"answer\":\"Current diagnostic methods have limitations in accuracy and efficiency, making consistent identification and early detection challenging.\"},{\"question\":\"What role do data preprocessing and feature engineering play in the proposed approach?\",\"answer\":\"They improve input data quality and enhance the model’s ability to produce more accurate predictions by selecting relevant features and preparing the dataset.\"},{\"question\":\"Which classifier and feature-selection strategy showed strong sensitivity/performance in the study?\",\"answer\":\"The findings indicate that the NB classifier using LASSO FS features achieved the highest sensitivity performance, and another model achieved high classification accuracy.\"}]","Heart Disease Detection by Machine Learning System - 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