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The neck dissection has remained a pivotal aspect of head and neck cancer management for over a century. During this time its role has expanded from a purely therapeutic option to an elective setting. Since vital anatomical structures are close, certain risks and complications are inherent to this procedure. Since neck surgery remains the most frequently performed form of therapeutic surgery in head and neck cancer irrespective of primary disease site, our objective is to report the complications in various types neck dissections and to seek improved outcome. A cross sectional retrospective study of 52 patients who underwent neck dissection from August 2015 to August 2019 was conducted to analyse intra operative and post-operative complications which aroused due to neck dissection. Indications for neck dissection depended on neck staging (N): selective neck dissection was done when evident disease was absent; Modified radical neck dissection was done if there was clinically evident neck node, preserving non-lymphatic neck structures (accessory nerve, internal jugular vein and internal jugular vein) as long as surgical completeness was not compromised. Bilateral neck dissection was indicated if contralateral disease was suspected or present. Out of 52 patients, one radical neck dissection, 14 modified radical and 37 selective neck dissection, of which 32 underwent supra omohyoid neck dissection and 5 underwent anterolateral and posterolateral neck dissection. The most frequent complication was marginal mandibular nerve injury (5.5%), followed by accessory nerve injury (2.1%). There was one death. A careful preoperative assessment of the patient, meticulous surgical techniques, good-quality postoperative care and appropriate rehabilitation are the cornerstones of preventing and managing complications of neck dissection.

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The Infection Prediction Framework is built on predictive displaying. The framework examines the client's side effects as well as their present and clinical history. System also determines the severity of infection and recommends treatment based on severity of the condition. It recommends a healthy diet and appropriate physical activity for the client. Expecting infection at a later stage becomes a considerable task. The Convolutional neural Network (CNN) model is used to anticipate such anomalies, as it can precisely identify information related to infection expectation from unstructured clinical health records. However, assuming that CNN uses a completely coupled network structure, it consumes a lot of memory. In addition, an increase in the number of layers might lead to an increase in the model's intricacy examination. The prediction of infection at an early stage becomes a critical task. This prediction can help people understand their potential stage of disease and take action accordingly as soon as possible. Prediction cannot be 100% correct as it is a probability statistic and cannot be always right. However, it can possibly be helpful in very serious situations and can lives. Keywords: Infection Prediction, Health Card, CNN (Convolutional Neural Networks), Smart disease prediction

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