Ireland Health News API

{
    "totalArticles": 199190,
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        {
            "id": "02a810b978e0a795fe8840a5b2c9c337",
            "title": "Machine learning-enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support",
            "description": "Electrocardiogram (ECG) signals play a critical role in the early detection of cardiac arrhythmias, which remain a major cause of morbidity and mortality worldwide. While deep learning approaches have achieved high classification accuracy, their increasing complexity often limits interpretability and practical applicability. This study presents a systematic and interpretable framework for multi-class ECG arrhythmia classification, examining the effects of signal processing, feature extraction, feature selection, and evaluation strategies on classification performance. Experiments were conducted on the MIT-BIH Arrhythmia Database using two feature representations: (i) morphological and temporal features, and (ii) a compact wavelet-based representation. Sequential Forward Feature Selection (SFFS) revealed that classification performance saturates at approximately 15 features, indicating that most discriminative information is captured within a compact subset. Using this feature space, the support vector machine (SVM) achieved the best overall performance, reaching 98.54% accuracy with stable results across different configurations. The wavelet-based representation further improved performance balance, yielding lower Golden Distance (GD) values (as low as 0.0249), indicating more consistent behavior across evaluation metrics. Overall, the results demonstrate that carefully designed feature extraction and selection enable classical machine learning methods, particularly SVM, to achieve high and reliable performance, providing an interpretable alternative to more complex black-box models in ECG arrhythmia classification.",
            "content": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give... [797 chars]",
            "url": "https://www.nature.com/articles/s41598-026-56828-4?error=cookies_not_supported&code=14eb9640-cd04-4cb8-bcee-ee6f987a8760",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-06-10T03:45:00Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        },
        {
            "id": "841c49b74b5a2bc6e4b6003e3bd44688",
            "title": "Cancer patients found a simple way to stay mentally sharp during chemotherapy",
            "description": "“Chemo brain” affects up to 80% of people receiving chemotherapy, making everyday tasks harder. In a new trial, cancer patients who followed a home-based exercise program showed better attention and fewer noticeable cognitive problems than those who received a placebo. Low-dose ibuprofen also improved some cognitive measures, though its effects were less consistent.",
            "content": "Now, a Phase II clinical trial suggests that two relatively simple interventions, exercise and low-dose ibuprofen, may help reduce some of these cognitive challenges. The study was published in CANCER, a peer-reviewed journal of the American Cancer S... [3270 chars]",
            "url": "https://www.sciencedaily.com/releases/2026/06/260608040019.htm",
            "image": "https://www.sciencedaily.com/images/1920/young-woman-cancer-chemotherapy.webp",
            "publishedAt": "2026-06-10T01:17:07Z",
            "lang": "en",
            "source": {
                "id": "81a05fd2712964dbdb32b1c6a27646fd",
                "name": "ScienceDaily",
                "url": "https://www.sciencedaily.com"
            }
        },
        {
            "id": "6b94e4757a728690f10b4d60a063b956",
            "title": "‘It’s relentless’: Mother caring for disabled twin children says respite ‘critical’ for carers",
            "description": "Seventy-one per cent of family carers face financial difficulty, Family Carers Ireland report shows",
            "content": "A woman who is caring for both of her 12-year-old twins has said she would describe her life at times as “not living but just existing”.\nCatherine Rossiter (52), from Stillorgan, south Dublin, cares for children Aoife and Eoghan, who were born premat... [2483 chars]",
            "url": "https://www.irishtimes.com/ireland/social-affairs/2026/06/09/its-relentless-mother-caring-for-disabled-twin-children-says-respite-critical-for-carers/",
            "image": "https://www.irishtimes.com/resizer/v2/PS34R2XQXZFJ3HJYUCDN2P26Q4.jpg?smart=true&auth=3c67b5668ac61a3e7ca90a0d67e52f70eb559665e63421767a21576e9661007e&width=1200&height=630",
            "publishedAt": "2026-06-09T20:02:43Z",
            "lang": "en",
            "source": {
                "id": "735252dcb2793bb57ad34b2a2b258f27",
                "name": "The Irish Times",
                "url": "https://www.irishtimes.com"
            }
        }
    ]
}