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In the past decades, there is a wide increase in the number of people affected by diabetes, a chronic illness. Early prediction of diabetes is still a challenging problem as it requires clear and sound datasets for a precise prediction. In this era of ubiquitous information technology, big data helps to collect a large amount of information regarding healthcare systems. Due to explosion in the generation of digital data, selecting appropriate data for analysis still remains a complex task. Moreover, missing values and insignificantly labeled data restrict the prediction accuracy. In this context, with the aim of improving the quality of the dataset, missing values are effectively handled by three major phases such as (1) pre-processing, (2) feature extraction, and (3) classification. Pre-processing involves outlier rejection and filling missing values. Feature extraction is done by a principal component analysis (PCA) and finally, the precise prediction of diabetes is accomplished by implementing an effective distance adaptive-KNN (DA-KNN) classifier. The experiments were conducted using Pima Indian Diabetes (PID) dataset and the performance of the proposed model was compared with the state-of-the-art models. The analysis after implementation shows that the proposed model outperforms the conventional models such as NB, SVM, KNN, and RF in terms of accuracy and ROC.

A Glimpse into Healthcare Policies: How Cancer Control and Prevention Programmes Have Evolved Across Asia.

Understanding Healthcare Policies in the Philippines: Cancer Care.

Healthcare Systems and Health Policies in Thailand: Cancer Care.

Cancer Care and Control in Taiwan: An Interview with Dr. Shu-Ti Chiou.

An Interview with Professor Myint Han: Healthcare in Myanmar.

Artificial intelligence (AI) technology is being leveraged for multiple tasks in the healthcare sector, such as improving the diagnosis of disease, streamlining management services, and tailoring treatment procedures. Applying predictive analysis and performing robotic surgery helps patients in a way that reduces the burden on their caregivers. This study uses a healthcare disease prediction dataset using three robust machine learning algorithms: random forest (RF), Gaussian Naïve Bayes (GNB), and support vector classifier (SVC). The models learn to use other symptoms to detect the presence of various diseases. In model evaluation, cross-validation is done on the training set after data preprocessing is performed to ensure none of the groups is overrepresented in the final model. In both the training and the testing of each model, which were respectively 100%, the model was able to make perfect predictions. A vote of three classifiers reached the 100% precision mark over a test dataset on an ensemble model that combined all the classifiers. This research integrates the advances in AI technology into the healthcare setting in a bid to enhance healthcare delivery. Additionally, we created such a straightforward tool in the case of input symptoms and proposed a possible disease diagnosis. This study also adds to the expanding corpus of research on AI in healthcare by providing a practical method for symptom-based diagnosis.

This study proposes a customized and reusable component-based design framework based on the UML modeling process for intelligent home healthcare systems. All the proposed functional components are reusable, replaceable, and extensible for the system developers to implement customized home healthcare systems addressing different demands of patients and caregivers from healthcare monitoring aspects. The prototype design of the intelligent healthcare system based on these proposed components can provide the following features: (1) monitoring and recording videos of rehabilitation situations and patient behavior using multiple CCD cameras, which can be stored accordingly in an archive; (2) recording the patient's physiological data and corresponding treatment plan, which can be stored in an XML archiving database for caregivers' review; (3) automatically alerting patients to remind them of medication schedules or treatment plans, while recording the patient's treatment situations; (4) caregivers monitoring videos and physiological records of the patient's rehabilitation using handheld mobile devices via the Internet or wireless communication networks; and (5) caregivers and patients establishing alert mechanisms for the patients' physiological warning states. If the patient's physiological state suddenly deteriorates, the module would immediately alert caregivers by sending notification messages to their remote mobile devices or web browsers.