[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118862-en":3,"doc-seo-118862-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},118862,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Automated Heart Syndrome Forecast Model - Exploiting Machine Learning Approaches","Heart disease is a common condition driven by poor diet and irregular lifestyle, causing millions of deaths worldwide each year. With many risk factors affecting cardiac outcomes, early diagnosis and accurate prognosis require reliable prediction methods. This study explores machine learning as an approach to retrieve critical information from medical databases and compares multiple algorithms to predict cardiac illness, presenting analytical findings and emphasizing different modeling methodologies for performance-focused decision support.","Automated Heart Syndrome Forecast Model Exploiting Machine Learning Approaches  \nParisha1, Gaurav Kumar Srivastava2, Santosh Kumar3  \n1Research Scholar: Dept. of Computer Science and Engineering  \nBabu Banarsi Das University  \nLucknow, India  \n[parisha369@gmail.com](parisha369@gmail.com)  \n2Assistant Professor: Dept. of Computer Science and Engineering  \nBabu Banarsi Das University  \nLucknow, India  \n3Associate Professor: School of Computing Science & Engineering  \nGalgotias University  \nGreater Noida, India  \n[Sant7783@hotmail.com](Sant7783@hotmail.com)  \nAbstract— Heart disease is a frequent condition that appears as a result of a poor diet and an irregular lifestyle. It is one of the most frequent diseases worldwide, with numerous reasons that damage the heart and have claimed countless lives in recent years. Due to the enormous number of risk factors for heart disease, it is critical to adopt a precise and dependable approach to provide an early diagnosis and correct prognosis. As a result, there is a broad potential for implementing various types of machine learning approaches for retrieving such critical data from the database. This study evaluates numerous machine learning algorithms for correctly predicting cardiac sickness and offers analytical findings, with an emphasis on various methodologies.  \nKeywords-Heart Infection, Machine Learning Techniques,, K-NN, Artificial Neural Network, Decision Trees.  \nI. INTRODUCTION  \nAround the previous decade, heart disease has been the leading cause of death all around the world. According to a study published by the World Health Organization, around 18-to-20 million people die each year as a result of heart disease. People from low- and middle-income countries are the most likely to die. Other habitual risk factors are smoking, alcohol overdoses, hypertension, and lack of physical activity.  \nData mining is a significant data extraction approach from massive datasets in today's society. To predict cardiac disease, regression techniques, clustering techniques, association rules, and classification strategies using the Nave Bayes algorithm, decision trees, random forests, and the K-nearest neighbours algorithm are just a few of the ways used. A medical dataset was acquired from different hospitals for this inquiry. As a result, this study is putting trendy techniques and algorithms for predicting and detecting various cardiac disorders to the test.  \nII. MOTIVATION/BACKGROUND  \nAround the previous decade, heart disease has been the leading cause of death all around the world. According to a study published by the World Health Organization, around 18-to-20 million people die each year as a result of heart disease. People from low- and middle-income countries are the most  \nlikely to die. Other habitual risk factors are smoking, alcohol overdoses, hypertension, and lack of physical activity.  \nData mining is a significant data extraction approach from massive datasets in today's society. To predict cardiac disease, regression techniques, clustering techniques, association rules, and classification strategies using the Nave Bayes algorithm, decision trees, random forests, and the K-nearest neighbours algorithm are just a few of the ways used. A medical dataset was acquired from different hospitals for this inquiry. As a result, this study is putting trendy techniques and algorithms for predicting and detecting various cardiac disorders to the test.  \nSeveral academics and scientists have previously done extensive work on cardiac problems in current settings. Let's start with some of their prior work on heart disease: Bhatla and Jyoti [1] sought to examine the various data mining approaches introduced in recent years for heart disease prediction. Dangare and Apte [2] investigated prediction methods for heart disease using a larger number of input attributes. Karthikeyan and Kanimozhi [3] suggested a Heart Disease Prediction System that uses a Deep Belief Network classification algori","cbCaidCd9SulncNG","https://ap.wps.com/l/cbCaidCd9SulncNG","pdf",257841,1,6,"English","en",105,"# Abstract\n# Introduction\n## Heart disease burden and risk factors\n## Data mining approaches for prediction\n# Motivation/Background\n## Prior research on heart disease prediction\n# Dataset\n## (Dataset description begins in the document)","[{\"question\":\"What problem does the model aim to address?\",\"answer\":\"The work targets accurate prediction of cardiac illness to support early diagnosis and prognosis despite the many risk factors associated with heart disease.\"},{\"question\":\"Which machine learning methods are highlighted for heart disease prediction?\",\"answer\":\"The document discusses multiple machine learning approaches, including Naive Bayes, decision trees, random forests, and K-nearest neighbors (K-NN), alongside other algorithmic comparisons.\"},{\"question\":\"What data is used in the study?\",\"answer\":\"A medical dataset is acquired from different hospitals, and the study analyzes emergent cardiac sickness based on the available clinical attributes.\"}]","Automated Heart Syndrome Forecast Model - 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