[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85066-en":3,"doc-seo-85066-105":28,"detail-sidebar-cat-0-en-105":89},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},85066,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Classifier Chain-based Pathological Test Recommendation","Accurate and timely diagnoses are essential for quality patient care, yet delayed test recommendations and physicians’ subjective interpretations can slow decision-making and increase costs. The study presents a pathological test recommendation system that selects appropriate diagnostic tests from patient symptoms before physician consultation. The task is formulated as multi-label classification using the Classifier Chain (CC) technique to model dependencies among tests. Multiple ML models are compared on a custom dataset built from SOUTHERN.IML pathology data, with Logistic Regression+CC reaching 98.83% accuracy and a Majority Voting ensemble balancing precision 0.93, recall 0.85, and F1-score 0.89. Explainable AI with SHAP provides symptom-to-test contribution reasoning consistent with established medical knowledge to support transparent clinical decisions.","Under Review  \nClassifier Chain-based Pathological Test  \nRecommendation  \nAbu Rafe Md Jamil∗† and Nayan Malakar∗‡  \n∗ Department of Computer Science and Engineering  \nJashore University of Science and Technology, Jashore–7408, Bangladesh  \n†Email: [arm.jamil@just.edu.bd](arm.jamil@just.edu.bd)  \n‡Email: [nayan.cse.just@gmail.com](nayan.cse.just@gmail.com)  \narXiv :2607 .08299v 1 [ cs .LG] 9 Jul 2026  \nAbstract—Accurate and timely diagnoses are essential for quality patient care. However, delayed recommendation of diagnostic tests and physicians’ subjective interpretations can hinder effective care. This study introduces a pathological test recommendation system that speeds up the test selection process using patient symptoms before physician consultation. The recommendation task is framed as a multi-label classification problem utilising the Classifier Chain (CC) technique to consider dependencies between tests. We collected data from the SOUTHERN.IML pathology and then created a custom dataset with the help of the expertise. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, were applied to compare models and identify the best fit for our study context. The Logistic Regression with CC model had the highest overall accuracy at 98.83%, while the Majority Voting ensemble model provided the best balance with a precision of 0.93, recall of 0.85, and F1-score of 0.89. To ensure transparency of the models and clinical interpretability, we used Explainable AI (XAI) techniques utilising SHAP (SHapley Additive Explanations), which identifies how each symptom is contributing toa test recommendation. The diagnostic reasoning revealed by the model was consistent with established medical knowledge of symptoms for the recommended tests, which further adds confidence to the model’s reliability for diagnostic purposes. The reasoning could help physicians make logical decisions in critical scenarios. Overall, our findings suggest that CC can improve the efficiency of the traditional algorithms in diagnostic process providing accurate test recommendations.  \nKeywords—Pathological Test, Multi-label Classification, Classifier Chain (CC), Ensemble Learning, Explainable AI  \nI. INTRODUCTION  \nA better treatment for patients depends on an accurate and timely diagnosis. However, the current diagnostic process is often inefficient and requires patients to visit the doctor multiple times, resulting in delayed decision-making. This prolongs treatment cycles and strains the medical systems. Furthermore, the test recommendation is controlled by the physicians, making the process subjective and inconsistent. Sometimes, unnecessary tests are prescribed, while critical ones are missed. This refers to delays in treatment and increased costs. We propose a machine learning system to recommend appropriate pathological tests based on a patient’s symptoms. Automating this process could reduce diagnosis delays, leading to better patient care, create a real-life dataset of patient’s histories, and make doctors’ jobs easier.  \nOur research targets a clear gap that most machine learning approaches in healthcare do not address. Most of them predict the disease directly without using any reliable pathological test and eliminating the involvement of any expert. This research  \naims to explore the intermediate step in predicting symptombased tests for timely, accurate diagnosis, and improved care. Currently, the pathological test suggestion is solely dependent on the physician’s decision, which can be subjective to their expertise and experience. By developing a system that recommends pathological tests, our aim is to support physicians, minimise inefficiencies, and improve the overall quality of care through advanced healthcare automation.  \nAlthough machine learning has been widely used in disease prediction and medical image analysis, less effort has been put into the optimisation of ordering pathological tests. 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does the system recommend pathological tests before physician consultation?","Question",{"text":73,"@type":74},"It takes a patient’s symptoms as input and frames test selection as a multi-label classification task, predicting which pathological tests should be ordered.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Why is the Classifier Chain (CC) technique used in this work?",{"text":78,"@type":74},"CC is used to capture dependencies between multiple test recommendations by linking classifiers sequentially, improving predictions compared with methods that treat labels independently.",{"name":80,"@type":71,"acceptedAnswer":81},"How is interpretability ensured for clinical use?",{"text":82,"@type":74},"Explainable AI is applied using SHAP to show how each symptom contributes to each recommended test, making the model’s reasoning transparent and aligned with medical 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