Titlebook: Computational Intelligence for Engineering and Management Applications; Select Proceedings o Prasenjit Chatterjee,Dragan Pamucar,Dilbagh Pa

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Intelligent System for Diagnosis of Pulmonary Tuberculosis Using Ensemble Methode with the target result. After all, only one model category is used, enhanced model performance through substituting the selected features with high sensitivity and low accuracy in clinical knowledge. The experimental analysis shows that the gradient boosting (GB) XG boosting model achieves the bes

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Application of Machine Learning Algorithms for Cataract Predictionmaking. Nowadays, in healthcare, AI is utilized to develop an automated system which is used for early detection and regular diagnostic applications. In medium-income countries like India, there is a backlog of over several millions blind people, and cataract is one of the primary reasons for blindn

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Strokes-Related Disease Prediction Using Machine Learning Classifiers and Deep Belief Network Modellligence medical engineering is one of the newest and fastest-growing branches of research. From the adoption of medical devices to diagnostic expert systems, it can be profitable in the field of healthcare. These gadgets and expert systems generate data that are multidimensional and erratic. The us

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Analysis and Detection of Fraudulence Using Machine Learning Practices in Healthcare Using Digital Tl upsurge during pandemic is virtualization of hospital operational strategies, recruitment, and care models. Virtual models can assist in bed shortages, spreading of germs, staff schedules, operating rooms, and monitor systems. These will help to enhance patient care and performance. Digital twin t

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Prediction and Analysis of Polycystic Ovary Syndrome Using Machine Learningctive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, had a significant impact on making it difficult to determine the exact aetiology of the disease. The exact cause of PCOS is not yet clear. Therefore, multiple features are respon

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Feature Selection for Medical Diagnosis Using Machine Learning: A Revieweleting redundant, superfluous, or noisy attributes. Improved learning performance, such as increased learning accuracy, lower processing costs, and better model interpretability, can all be attributed to feature selection. Without the proper utilization of FS approach, machine learning models can g

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