首页|University of Dhaka Reports Findings in Heart Disease (Precision healthcare: A d eep dive into machine learning algorithms and feature selection strategies for a ccurate heart disease prediction)
University of Dhaka Reports Findings in Heart Disease (Precision healthcare: A d eep dive into machine learning algorithms and feature selection strategies for a ccurate heart disease prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Heart Disorders and Diseases-He art Disease is the subject of a report. According to news reporting from Dhaka, Bangladesh, by NewsRx journalists, research stated, "This paper presents a compr ehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research shed s light on optimal strategies for early detection." The news correspondents obtained a quote from the research from the University o f Dhaka, "MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precisi on and recall metrics emphasized for robust predictions. Our study addresses cha llenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate fe ature selection-play a crucial role in identifying relevant features and reducin g data dimensionality. Our findings showcase the impact of these techniques on i mproving prediction accuracy. Optimized models for each dataset have been achiev ed through grid search hyperparameter tuning, with configurations meticulously o utlined. Notably, a remarkable 99.12 % accuracy was achieved on th e first Kaggle dataset, showcasing the potential for accurate HDP. Model robustn ess across diverse datasets was highlighted, with caution against overfitting. T he study emphasizes the need for validation of unseen data and encourages ongoin g research for generalizability. Serving as a practical guide, this research aid s researchers and practitioners in HDP model development, influencing clinical d ecisions and healthcare resource allocation."
DhakaBangladeshAsiaAlgorithmsCar diovascular Diseases and ConditionsCyborgsEmerging TechnologiesHeart Disea seHeart Disorders and DiseasesMachine Learning
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