[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124280-en":3,"doc-seo-124280-105":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},124280,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predictive Modeling of Patient Outcomes Using Machine Learning Algorithms in Health Informatics - Research Paper","Rapid progress in health informatics applies machine learning to build predictive models for forecasting patient results. The research examines how ML algorithms support healthcare decision making by estimating wellness status, hospital readmission needs, and disease progression. Comparative evidence indicates ML models achieve higher precision than traditional statistical approaches. The study also addresses implementation obstacles including data quality, interpretability, and ethical concerns, while evaluating model performance and practical suitability in clinical systems. ","20(1): 516-522, 2025  \n[www.thebioscan.com](www.thebioscan.com)  \nPredictive Modeling of Patient Outcomes Using Machine Learning Algorithms in Health Informatics  \n1 Dr. Mage Usha U,  \nAssociate Professor, Department of Computer Applications, Rajarajeswari College of Engineering, Kumbalgodu, Mysore Road, Bangalore-560074  \n[mageusha@gmail.com](mageusha@gmail.com)  \n2 Dr. A. M. Arun Mohan,  \nAssociate Professor, Department of Civil Engineering, Sethu institute of Technology,College Pulloor,Kariapatti, Tamilnadu- 626115  \n[arunmohan@sethu.ac.in](arunmohan@sethu.ac.in)  \n3 Dr. Trupti Kaushiram Wable,  \nAssistant Professor, Department of Electronics and Computer Engineering,Sir Visvesvaraya Institute of Technology, Nashik- 422102  \n[wabletrupti@gmail.com](wabletrupti@gmail.com)  \n4 Mr. Narayanam. P.S. Acharyulu  \nAssistant Professor, Department of Engineering Physics, S.R.K.R. Engineering College,Bhimavaram  \n[phanisat2010@gmail.com](phanisat2010@gmail.com)  \n5 Dr Dola Sanjay S,  \nProfessor and Dean, Department of ECE, Aditya University, Kakinada, AP  \n[dicedola@gmail.com](dicedola@gmail.com)  \nDOI: [https://doi.org/10.63001/tbs.2025.v20.i01.pp516-522](https://doi.org/10.63001/tbs.2025.v20.i01.pp516-522)  \nKEYWORDS  \nPredictive modeling, machine learning, health informatics, patient outcomes, electronic health records, clinical decision support  \nReceived on: 04-01-2025  \nAccepted on: 04-02-2025 Published on:  \n10-03-2025  \nABSTRACT  \nThe quick development of health informatics technology now utilizes machine learning (ML) methods to improve predictive models that forecast patient results. A comprehensive research analyzes how ML algorithms predict healthcare situations including patient wellness status and hospital re-entry needs and disease advancement tracking. ML models show better ability to predict patient outcomes with higher precision than established statistical solution techniques. The text explores both the practical obstacles related to data quality and interpretability as well as ethical issues faced by ML models. Research confirms that ML demonstrates its ability to transform personalized medical care as well as clinical choice processes.  \nINTRODUCTION  \nThe development of healthcare systems containing complex technologies has resulted in massive patient data growth which stems from electronic health records (EHRs) together with wearable health devices and medical imaging. The complete optimization of extensive health data stores provides important benefits to enhance medical treatments and lower hospital return rates along with improving clinical choices. Logistic regression together with survival analysis remain popular methods which healthcare institutions use to develop predictive models [1-2] . Through machine learning technology which belongs to artificial intelligence (AI) predictive analytics has transformed data pattern finding in extensive datasets. ML models leverage structure and  \nunstructured data types to derive insights which assist medical staff before disease diagnosis as well as disease evolution tracking and individualized therapeutic strategies creation. ML implementations in health informatics demonstrate their effectiveness through predictions of hospital-acquired infections and sepsis as well as heart failure and cancer prognosis. ML algorithms succeed in precision medicine because they can use historical patient information to develop adaptations to newer healthcare facts.  \nHealthcare ML accomplishments have become better through recent improvements in deep learning methods together with ensemble learning and natural language processing (NLP) methods. Deep learning models composed of CNNs and RNNs  \nsuccessfully analyze medical imaging together with time-series patient information for predicting health decline and disease relapse. Real-world clinical decision support systems now make use of ML-powered technology to assist physicians with evidencebased decision making as a result of these ad","cbCaidt6lNSpnOLt","https://ap.wps.com/l/cbCaidt6lNSpnOLt","pdf",538329,1,7,"English","en",105,"# Introduction\n## Healthcare data growth and predictive modeling\n## ML evolution in health informatics\n## Challenges: privacy, interpretability, fairness\n## Study scope and evaluation approach","[{\"question\":\"What does the paper aim to achieve in health informatics?\",\"answer\":\"It aims to systematically evaluate machine learning algorithms for predicting patient outcomes and assessing their suitability for real healthcare settings.\"},{\"question\":\"Which kinds of patient outcomes does the study focus on?\",\"answer\":\"The paper considers predicting patient wellness status, hospital re-entry (readmission) needs, and disease advancement over time.\"},{\"question\":\"What implementation challenges are highlighted for clinical use of ML models?\",\"answer\":\"The paper highlights privacy, interpretability, and fairness concerns, along with data issues such as missing values and imbalanced classes that can bias results.\"}]","Predictive Modeling of Patient Outcomes Using Machine Learning Algorithms in Health Informatics - 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