[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128076-en":3,"doc-seo-128076-105":31,"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128076,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Methods for Prediction of Brain Tumors and Pneumonia Diseases","Pneumonia and brain tumors present major challenges for timely and accurate diagnosis, especially at early stages. This study uses machine learning on medical imaging to detect informative patterns in input images and automatically classify disease conditions. The work predicts and classifies pneumonia and brain tumors, compares the performance of Decision Tree, SVM, KNN, Random Forest, Logistic Regression, and Naïve Bayes, and evaluates how increasing dataset size influences classification quality. Random Forest achieves the best results, reaching 90% accuracy for brain tumors and 79% for pneumonia.","Machine Learning Methods for Prediction of Brain Tumors and Pneumonia Diseases  \nKhadija EL Haddad  \nCadi Ayyad University, National School of Applied Sciences  \nSafi, Morocco  \n[khadija.elhaddad@ced.uca.ma](khadija.elhaddad@ced.uca.ma)  \nAissam Bekkari  \nCadi Ayyad University, National School of Applied Sciences  \nMarrakech, Morocco  \nA.BEKKARI@UCA.MA  \nWalid Bouarifi  \nCadi Ayyad University, National School of Applied Sciences  \nMarrakech, Morocco  \nW.BOUARIFI@UCA.MA  \nAbstract—Pneumonia and brain tumors are considered critical diseases due to the substantial challenges related to accurate prediction and diagnosis at an early stage. Machine learning (ML) methods are used in medical imaging processing to detect specific patterns and features within input images and automatically classify various medical conditions. This paper aims to predict and classify pneumonia and brain tumors diseases, to compare the ML performance of methods: Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), andNaïve Bayes (NB), and to analyze the impact of dataset increasing size on the classification performance. This study reveals that the Random Forest algorithm achieves the best performance, with 90% accuracy in the brain tumors dataset and 79% accuracy in pneumonia disease prediction.  \nKeywords-ML, Classification, Medical Images, RF, SVM, NB, KNN, LR, DT, Machine Learning  \nI. INTRODUCTION  \nNowadays many factors such as the significant growth of medical data amount, noise, and texture make manual image processing slow and inefficient. Machine learning techniques have proven efficiency and contribute to improving medical analysis solutions. Machine learning as an artificial intelligence subcategory is one of the rapidly growing domains [1] of computer science. Reinforcement, supervised, unsupervised, and semi-supervised learning are the four main groups of machine learning [2] . The process of machine learning is iterative and based on five stages:  \n• Data Collection: consists of collecting data from diverse sources and formats  \n• Data Processing: in this stage, the data is refined and features are extracted  \n• Model Building: for this step, the machine learning model is selected and data is trained and validated.  \n• Model evaluating: based on the model’s evaluation, following each model’s approach.  \n• Model Deployment: in the deployment phase, the model is integrated into the production environment for decisionmaking.  \nFigure 1 illustrates the steps of the machine learning process:  \nFigure 1: Machine Learning Process  \nMachine learning techniques have widespread applications [1] the most fundamental of which is data mining including classification, clustering, regression anomalies detection, and more in various fields such as business, economy, marketing,  \ncomputer vision, healthcare, and others. In medical fields, machine learning techniques classification consists of extracting valuable data and features for accurate medical conditions prediction.  \nII. RELATED WORK  \n[3] applied the machine learning techniques: Extreme Gradient Boost (XGB) classifier, SVM, Decision Tree, Gaussian Naive Bayes, Random Forest, Stochastic Gradient Descent (SGD) classifier, Bagging classifier, and LGBM classifier for Brain Tumors presence prediction and demonstrated that all classifiers are giving good results.  \nPediatric brain tumor segmentation and classification in [4] explores the efficiency of combining two innovative texture characteristics alongside multimodal magnetic resonance image intensity, the Self Organizing Map (SOM) has proved 90% for this study.  \nIn the [5] study, the classification of brain X-rays using classifiers SVM and KNN into malignant Vs. Benign categories use the GLCM method for extracting features from images. The results demonstrated 96% and 86% accuracy for SVM and KNN respectively.  \nIn the research paper [6], the classification of Pneumonia is based o","cbCaitvug2sMI6OM","https://ap.wps.com/l/cbCaitvug2sMI6OM","pdf",541515,3,1,7,"English","en",105,"# Introduction\n# Related Work\n# Methodology\n## Research Approach","[{\"question\":\"Which machine learning methods are compared for predicting pneumonia and brain tumors?\",\"answer\":\"Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Naïve Bayes are compared for the prediction and classification tasks.\"},{\"question\":\"What datasets are used in the methodology?\",\"answer\":\"The study uses a Brain Tumors MRI dataset and a Pneumonia MRI dataset to train and validate supervised models.\"},{\"question\":\"How does the Random Forest model perform compared with other methods?\",\"answer\":\"Random Forest provides the best performance, achieving 90% accuracy on the brain tumors dataset and 79% accuracy for pneumonia disease prediction.\"}]","Machine Learning Methods for Prediction of Brain Tumors and Pneumonia Diseases | 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machine learning methods are compared for predicting pneumonia and brain tumors?","Question",{"text":76,"@type":77},"Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Naïve Bayes are compared for the prediction and classification tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What datasets are used in the methodology?",{"text":81,"@type":77},"The study uses a Brain Tumors MRI dataset and a Pneumonia MRI dataset to train and validate supervised models.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the Random Forest model perform compared with other methods?",{"text":85,"@type":77},"Random Forest provides the best performance, achieving 90% accuracy on the brain tumors dataset and 79% accuracy for pneumonia disease 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