[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126065-en":3,"doc-seo-126065-105":30,"detail-sidebar-cat-0-en-105":92},{"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},126065,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Predicting Chronic Liver Disease and Jaundice Detection - an Integrated Approach Using Statistical Feature Extraction and Machine Learning Algorithms","Chronic liver disease requires early identification to support timely management and therapy. This research proposes an integrated approach for jaundice identification and chronic liver disease forecasting by combining projection-based statistical feature extraction with machine learning. Patient data features such as test results and clinical biomarkers are extracted, then classified using ANN, KNN, and Naive Bayes. Model quality is evaluated via F1 score, recall, accuracy, and precision to improve diagnostic accuracy and enable earlier interventions.","Predicting Chronic Liver Disease and Jaundice Detection an Integrated Approach Using Statistical Feature Extraction and Machine Learning  \nAlgorithms  \nM. Gajalakshmi1, Dr. C. Priya2  \n1Research Scholar, Department of Computer Applications, Dr.M. G.R. Educational Research Institute, Chennai, TamilNadu, India, [mglmca@gmail.com](mglmca@gmail.com)  \n2Professor and Research Supervisor, Faculty of Computer Applications, Dr.M. G.R. Educational Research Institute, Chennai, TamilNadu, India, [drcpriya.research@gmail.com](drcpriya.research@gmail.com)  \nKEYWORDS  \nArtificial Neural Networks, KNN, Naive Bayes.  \nABSTRACT  \nAn important health concern with chronic liver disease is early identification, which is essential for management and therapy. This work suggests an integrated method to identify tattoo-induced jaundice and forecast chronic liver disease by combining statistical feature extraction with machine learning techniques. First, we use statistical techniques based on projections to extract pertinent aspects from patient data, such as test results and clinical markers. We then use Artificial Neural Networks (ANN), KNN, and Naive Bayes to categorize individuals according to the probability that they have chronic liver illness and to pinpoint jaundice episodes associated with tattooing. The KNN technique offers interpretability, managing categorical data is made simple by Naive Bayes, and complicated patterns are captured by ANN using layers of neural networks. These models' performance is assessed using the following metrics: F1 score, recall, accuracy, and precision. Our research aims to improve prediction accuracy and offer practical advice for early diagnosis and individualized treatment plans. By utilizing advanced data analysis and machine learning instruments, the integrated strategy shows promise in enhancing healthcare outcomes.  \n1. Introduction  \nA serious health issue, chronic liver disease is gradual liver impairment that can result in life-threatening consequences and a diminished quality of life. Improving patient outcomes and effectively managing this chronic illness require early identification and prompt action. The necessity for non-invasive and reasonably priced diagnostic tools is highlighted by the potential invasiveness and expense of traditional diagnostic procedures such as liver biopsies and imaging technologies.  \nCurrent advancements in data analytics and machine learning have created new opportunities for illness detection and prediction. It is possible to find patterns and correlations that might not be obvious using old methods by utilizing big datasets and complex algorithms. This work integrates projection-based statistical feature extraction to improve the prediction of chronic liver disease.  \nTechniques for statistical feature extraction are used to handle and examine patient data, including bilirubin concentration, liver enzyme levels, and other biomarkers. To create precise prediction models, these characteristics are essential. Machine learning algorithms operate more effectively and efficiently when projection-based methods, such as To reduce the dimensionality of the data while keeping crucial information, Principal Component Analysis (PCA) is utilized.  \nThis study covers the identification of jaundice, especially when it is produced by tattooing, in addition to predicting chronic liver disease. Liver malfunction may be indicated by jaundice, a disorder marked by yellowing of the skin and eyes. Jaundice from tattoos can occasionally make the diagnosis more difficult to make, necessitating the distinction between jaundice from chronic liver illness and other causes.  \nThe processed data is analyzed using machine learning methods KNN, Naive Bayes, and Artificial Neural Networks (ANN) . Each algorithm offers distinct advantages: KNNs provide clear interpretability of classification rules, Naive Bayes handles categorical data with ease, and ANNs capture complex relationships through multiple","cbCairsUudquFUhJ","https://ap.wps.com/l/cbCairsUudquFUhJ","pdf",279362,5,1,"English","en",105,"# 1. Introduction\n## Motivation and need for early non-invasive diagnosis\n## Projection-based feature extraction and PCA\n## Machine learning models and evaluation metrics\n# 2. Literature Survey\n## Feature reduction and statistical methods\n## Machine learning approaches and reported performance","[{\"question\":\"What is the core idea of the proposed approach?\",\"answer\":\"It combines projection-based statistical feature extraction from patient data with machine learning classifiers to forecast chronic liver disease and detect tattoo-related jaundice.\"},{\"question\":\"Which machine learning algorithms are used for classification?\",\"answer\":\"Artificial Neural Networks (ANN), KNN, and Naive Bayes are used to categorize individuals based on chronic liver illness probability and to pinpoint jaundice episodes.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is assessed using F1 score, recall, accuracy, and precision to measure prediction quality for both chronic liver disease and jaundice detection.\"}]","Predicting Chronic Liver Disease and Jaundice Detection - an Integrated Approach Using Statistical Feature Extraction and Machine Learning Algorithms | 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is the core idea of the proposed approach?","Question",{"text":76,"@type":77},"It combines projection-based statistical feature extraction from patient data with machine learning classifiers to forecast chronic liver disease and detect tattoo-related jaundice.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are used for classification?",{"text":81,"@type":77},"Artificial Neural Networks (ANN), KNN, and Naive Bayes are used to categorize individuals based on chronic liver illness probability and to pinpoint jaundice episodes.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated in the study?",{"text":85,"@type":77},"Performance is assessed using F1 score, recall, accuracy, and precision to measure prediction quality for both chronic liver disease and jaundice 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