[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118993-en":3,"doc-seo-118993-105":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},118993,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Prediction of Disease in the HealthCare using Machine Learning - Heart Disease Diagnosis with Balanced Data","Heart disease is a complex and widely prevalent condition, making timely and efficient identification essential in cardiology. The proposed work develops an accurate machine learning–based diagnostic system for heart disease prediction, with special focus on the common challenge of imbalanced datasets. It discusses how imbalance can bias models toward the majority class and hide poor detection of diseased cases. To address this, the approach balances the dataset and then trains supervised classification models, comparing their performance to improve generalization for both presence and absence of heart disease.","Prediction of disease in the HealthCare using Machine  \nLearning  \nMrs. Yerraginnela Shravani1, Dr Ashesh K2  \n1PhD-Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.  \n2Associate Professor, Department ofCSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.  \n[shravanilokeshwar@gmail.com1](shravanilokeshwar@gmail.com1), [imasheshk@kluniversity.in2](imasheshk@kluniversity.in2)  \nAbstract  \nHeart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. Predicting diseases in healthcare using machine learning often encounters imbalanced datasets where the number of instances of one class (e.g., diseased patients) is significantly lower than the other (e.g., non-diseased patients) . Addressing imbalanced data is crucial as models trained on such datasets tend to favor the majority class, leading to biased predictions.in this project also doing imbalce data to balance data using Random Under and over Sampler. after conversion of data into balanced data.  \nKeywords: Heart disease, Diagnosis, Machine learning, Classification algorithms, Data imbalance, Random Under Sampler, Random Over Sampler, SMOTE, Balanced data, Healthcare, Cardiology.  \nI. INTRODUCTION  \nIn the realm of modern healthcare, the accurate prediction and timely identification of diseases have become increasingly vital. Among these diseases, heart disease stands out as a complex and prevalent health issue, impacting a substantial portion of the global population. Given its widespread prevalence and the potential severity of its consequences, the need for effective tools to diagnose heart disease cannot be overstated, particularly within the field of cardiology.  \nThis article introduces an innovative approach to address this pressing concern—a system that harnesses the power of machine learning techniques for the precise diagnosis of heart disease. The application of machine learning in healthcare has garnered considerable attention due to its potential to revolutionize medical diagnostics. In this context, our system represents a significant advancement towards more efficient and accurate disease prediction.  \nBy employing these techniques, we aim to enhance the system's capacity to discern patterns and relationships within complex medical data, ultimately leading to more reliable diagnoses.  \nThe consequences of imbalanced data in this context are significant. A model trained on imbalanced data may have a tendency to classify most instances as the majority class (e.g., no heart disease), resulting in a high accuracy score that mask poor performance in detecting cases of heart disease. Therefore, it becomes imperative to preprocess the data effectively to ensure that the model is sensitive to both [classes. compare](classes. compare) several strategies for balancing the dataset,  \nincluding under sampling the majority class, oversampling the minority class using synthetic data generation techniques like SMOTE, and hybrid approaches. By achieving a balanced distribution of classes, we aim to improve the model's ability to generalize to both instances of heart disease presence and absence. the dataset and its characteristics, the challenges posed by imbalanced data, the methodologies employed for balancing the data, the experimental setup, presents and analyzes the results. Finally, future directions for refining heart disease prediction models in the presence of imbalanced data.  \nTo tackle this issue, we implement the Random Under Sampler method, which helps balance the dataset by reducing the number of instances in the majority class. This approach ensures that our machine learning models are trained on amore ","cbCaibyPYxdrjDA4","https://ap.wps.com/l/cbCaibyPYxdrjDA4","pdf",668322,1,11,"English","en",105,"# Abstract\n# Introduction\n## Challenges of imbalanced datasets\n## Data balancing with Random Under Sampler\n## Supervised classification and algorithm comparison\n# Literature Survey","[{\"question\":\"Why is predicting heart disease important in healthcare?\",\"answer\":\"Heart disease is complex and highly prevalent, so timely diagnosis is critical to reduce severe outcomes. Accurate prediction supports more efficient cardiology decision-making.\"},{\"question\":\"What problem does the approach address in machine learning for disease prediction?\",\"answer\":\"The approach addresses imbalanced datasets, where one class (e.g., diseased patients) has far fewer instances than the other. This imbalance can cause biased predictions favoring the majority class.\"},{\"question\":\"How is the dataset balanced before training the model?\",\"answer\":\"The method uses Random Under Sampler to reduce the majority class and achieve a more equitable class distribution. After conversion to balanced data, supervised models are trained for heart disease classification.\"}]","Prediction of Disease in the HealthCare using Machine Learning - Heart Disease Diagnosis with Balanced Data | PDF",1785721517,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-disease-in-the-healthcare-using-machine-learning-heart-disease-diagnosis-with-balanced-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-disease-in-the-healthcare-using-machine-learning-heart-disease-diagnosis-with-balanced-data/118993/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting heart disease important in healthcare?","Question",{"text":75,"@type":76},"Heart disease is complex and highly prevalent, so timely diagnosis is critical to reduce severe outcomes. Accurate prediction supports more efficient cardiology decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the approach address in machine learning for disease prediction?",{"text":80,"@type":76},"The approach addresses imbalanced datasets, where one class (e.g., diseased patients) has far fewer instances than the other. This imbalance can cause biased predictions favoring the majority class.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset balanced before training the model?",{"text":84,"@type":76},"The method uses Random Under Sampler to reduce the majority class and achieve a more equitable class distribution. 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