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"Penyakit jantung, seperti yang didefinisikan oleh World Health Organization (WHO) sebagai kumpulan berbagai gangguan yang memengaruhi kesehatan jantung, merupakan salah satu masalah kesehatan global yang memerlukan deteksi dini dan penanganan yang efektif. Faktor risiko yang menyebabkan penyakit jantung seperti usia, jenis kelamin, nyeri dada (chest pain), tekanan darah (resting blood pressure), kolesterol (cholesterol), kadar gula darah (fasting blood sugar), hasil elektrokardiogram (resting electrocardiogram), detak jantung maksimum yang dicapai (maximum heart rate achieved), keberadaan angina yang diinduksi (exercise-induced angina), depresi segmen ST (oldpeak), bentuk kelengkungan pada kurva (slope of the peak exercise st segment), jumlah pembuluh darah utama yang diwarnai oleh flourosopy (Number of Major Vessels Colored by Fluoroscopy/CA), dan jenis thalassemia (thalassemia), memiliki peran signifikan dalam meningkatkan risiko terjadinya penyakit jantung. Penelitian ini dilakukan menggunakan dataset yang berasal dari Klinik Cleveland, yang terdiri dari 303 entri data. Dataset ini digunakan untuk melakukan deteksi terhadap keberadaan atau ketidakhadiran penyakit jantung berdasarkan sejumlah atribut klinis yang diukur. Atribut-atribut ini, atau fitur-fitur, mencakup berbagai informasi seperti tekanan darah, kadar kolesterol, usia, jenis kelamin, dan lainnya. Tujuan dari penelitian ini adalah untuk melakukan deteksi yang dapat memprediksi penyakit jantung berdasarkan informasi klinis pasien dengan akurasi terbaik. Untuk mencapai tujuan ini, model Random Forest dilatih dan dibandingkan dengan model lain meliputi Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Neural Network dan XGBoost. Hasil evaluasi menunjukkan bahwa Random Forest Algorithm memiliki akurasi yang paling tinggi, mencapai 96,77%. Ini berarti bahwa model Random Forest mampu memprediksi keberadaan atau ketidakhadiran penyakit jantung dengan tingkat keberhasilan yang sangat tinggi. Sebagai hasilnya, Random Forest dipilih sebagai model yang paling sesuai untuk melakukan deteksi penyakit jantung dalam dataset ini. Model ini diharapkan dapat memberikan kontribusi yang signifikan dalam deteksi dini, pencegahan, dan pengelolaan penyakit jantung secara global.

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Heart disease, as defined by the World Health Organization (WHO) as a collection of various disorders affecting heart health, is one of the global health issues requiring early detection and effective management. Risk factors contributing to heart disease such as age, gender, chest pain (angina), blood pressure (trestbps), cholesterol (cholesterol), blood sugar levels (fbs), electrocardiogram results (restecg), maximum heart rate achieved (thalach), presence of induced angina (exang), ST segment depression (oldpeak), slope of the ST segment (slope), number of major vessels colored by fluoroscopy (ca), and type of thalassemia (thal), play a significant role in increasing the risk of heart disease. This research was conducted using a dataset obtained from the Cleveland Clinic, consisting of 303 data entries. This dataset was utilized to classify the presence or absence of heart disease based on various measured clinical attributes, including blood pressure, cholesterol levels, age, gender, among others. The aim of this study is to perform detection that can predict heart disease based on patient clinical information with the highest accuracy. To achieve this objective, the Random Forest model was trained and compared with other models, including Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Neural Network, and XGBoost. Evaluation results demonstrate that the Random Forest Algorithm achieved the highest accuracy, reaching 96,77%. This implies that the Random Forest model can predict the presence or absence of heart disease with a very high success rate. Consequently, Random Forest was chosen as the most suitable model for classifying heart disease in this dataset. This model is anticipated to significantly contribute to the early detection, prevention, and management of heart disease globally."

"ABSTRAKDalam beberapa dekade terakhir, pencitraan sinar-X menggunakan film-screen mulaidigantikan oleh digital radiography. Sistem pencitraan digital salah satunya adalahcomputed radiography (CR). Sejauh ini di Indonesia, perkembangan yang pesat dari CRbelum dibarengi dengan penelitian untuk memperoleh kondisi optimum dalamaplikasinya.Telah dilakukan penelitian di RS X menggunakan CR Agfa tipe PSP MD 4.0 danfantom Rando Man untuk menentukan optimasi pembentukan citra. Juga dilakukanpengukuran Entrance Surface Dose (ESD) menggunakan thermoluminescent dosimeter(TLD) dengan berbagai variasi nilai kV. Pemeriksaan yang dipilih adalah kepala PA,thorax PA, dan abdomen AP. Citra fantom dievaluasi berdasarkan panduan dariEuropean Commission dibantu oleh dokter spesialis radiologi. Optimasi citra didasarkanpada nilai kV dengan nilai ESD yang rendah dan hasil evaluasi citra.Hasil penelitian menunjukkan bahwa untuk pemeriksaan kepala PA optimasi terjadipada ESD 3,580 mGy dan 3,834 mGy untuk kondisi 80 kV dan 83 kV dengan 0,224 ?0,274 mGy/mAs. Untuk pemeriksaan thorax PA teknik kV standar optimasi terjadi padaESD 1,341 mGy dan 2,378 mGy untuk kondisi 50 kV dan 55 kV dengan 0,134 ? 0,297mGy/mAs. Sedangkan untuk teknik kV tinggi yang menggunakan 100 kV, optimasiterjadi pada ESD 2,960 mGy dengan 0,947 mGy/mAs. Dan untuk pemeriksaanabdomen AP optimasi terjadi pada ESD 4,090 mGy dan 4,268 mGy untuk kondisi 70kV dan 80 kV dengan 0,204 ? 0,267 mGy/mAs. Selain nilai kV, optimasi jugamengikutsertakan nilai kontras tinggi dan rendah, serta karakter CR Agfa yang diwakilioleh nilai lgM (log Median).

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AbstractFor the last few decades, X-ray imaging using film screen has been replaced by digitalradiography. One of digital imaging systems is computed radiography (CR). So far inIndonesia, the rapid development of CR is not ensued with research to obtain optimumcondition in its application.Has been performed a research in hospital X using Agfa CR Type PSP MD 4.0 andRando Man phantom to determine optimization of image development. Also conductedmeasurement of Entrance Surface Dose (ESD) using thermoluminescent dosimeter(TLD) for various kV values. The examinations were selected for skull PA, thorax PA,and abdomen AP. Image phantom assessment was carried out using guideliness fromEuropean Commission with assistance of radiologist. Optimization of image was donebased on kV value with low ESD value and image assessment.The results showed that for skull PA examination, optimization occured on ESD 3.580mGy and 3.834 mGy for exposure condition of 80 kV and 83 kV with 0.224 to 0.274mGy/mAs. For standard kV technique thorax PA examination, optimization occured onESD 1.341 mGy and 2.378 mGy at 50 kV and 55 kV with 0.134 to 0.297 mGy/mAs. Asfor the high kV technique of which used a 100 kV, ESD optimization occured at 2.960mGy with 0.947 mGy/mAs. While for abdomen AP examination, optimization occuredon ESD 4.090 mGy and 4.268 mGy for 70 kV and 80 kV with 0.204 to 0.267mGy/mAs. In addition to values of kV, optimization also included high and low contrastvalues as consideration and Agfa CR character that was represented by the lgM (logMedian) value."

"Advances in imaging devices and Image processing stem from cross-fertilization between many fields of research such as chemistry, physics, mathematics and computer sciences. This bioImaging community feel the urge to integrate more intensively its various results, discoveries and innovation into ready to use tools that can address all the new exciting challenges that life scientists (Biologists, Medical doctors ...) keep providing, almost on a daily basis. Devising innovative chemical probes, for example, is an archetypal goal in which image quality improvement must be driven by the physics of acquisition, the image processing and analysis algorithms and the chemical skills in order to design an optimal bioprobe."

"This book addresses patient-specific modeling. It integrates computational modeling, experimental procedures, imagine clinical segmentation and mesh generation with the finite element method (FEM) to solve problems in computational biomedicine and bioengineering. Specific areas of interest include cardiovascular problems, ocular and muscular systems and soft tissue modeling. "

"The three-volume set LNCS 7510, 7511, and 7512 constitutes the refereed proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, held in Nice, France, in October 2012. Based on rigorous peer reviews, the program committee carefully selected 252 revised papers from 781 submissions for presentation in three volumes. The first volume includes 91 papers organized in topical sections on abdominal imaging, computer-assisted interventions and robotics, computer-aided diagnosis and planning, image reconstruction and enhancement, analysis of microscopic and optical images, computer-assisted interventions and robotics, image segmentation, cardiovascular imaging, and brain imaging, structure, function and disease evolution."

"Creating new medical ontologies for image annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system, cross media relevance models. Based on a text query the system will retrieve the images that contain objects described by the keywords."