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In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.
RESUMO
INTRODUCTION: Cancer is a major life-threatening disease and has an impact on both patients and their family members. Caring for cancer patients may lead to several levels of stress which may affect their own health as well as their quality of life. AIM: To assess the perceived stress and burden of family caregivers of head and neck cancer patients (HNC) attending cancer care centre at a tertiary care centre, Tamil Nadu. OBJECTIVES: To assess the perceived stress and the burden among caregivers of patients with head and neck cancer using the Perceived Stress Scale (PSS) and Caregiver Strain Index (CSI) respectively. MATERIALS AND METHOD: A Cross-sectional study was carried out for a period of three months among the caregivers of head and neck cancer patients at a cancer care centre, Madurai. A total of 200 caregivers were selected by Convenience sampling method. Data was collected using a pretested, self-structured, closed-ended questionnaire by face to face interview method. RESULTS: The study population consisted of Caregivers aged 21-60 years, mostly females (80%), spouses (54%), employed (57%) and uneducated (66%). Most of the caregivers were from lower socioeconomic status (66%) and those who are providing care for 1 to 6 months were more in number. In this study, 82% of caregivers reported high caregiver burden (CSI ≥7) and 67% of caregivers reported high stress (PSS ≥ 26 - 40). CONCLUSION: Caregivers are experiencing significant burden, particularly with respect to their physical and psychological well-being, economic circumstances, social and personal relationships.