Project Report (Bachelor of Science in Computer Science)

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Subject: search.filters.subject.Machine learning

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    Mortality risk and health suggestions for critical patients using extended LSTM and CNN
    (BRAC University, 2025) Ashraf, Muaz Ibne; Islam, S.M. Sihat; Siraz, Zasia Farzin; Rahman, Md. Khalilur
    Intensive care units (ICUs) and their high mortality rate often require predictive tools that could help to determine at-risk patients in time and direct the interventions. The given project presents a deep learning system that merges Convolutional Neural Networks (CNN) with the Long Short Term Memory (LSTM) networks to better predict the risk of mortality at an early stage among critical patients in ICUs. Using a significant portion of the patient data such as the vital signs and laboratory results, the model can carry out constant risk analysis and it could prove superior to the conventional scoring systems. Health suggestion module is also incorporated to make suggestions on clinical interventions to be made on high risk patients thus assisting healthcare providers with their decision-making. Finally, the suggested direction will enhance the patient outcomes as it will allow delivering proactive medical care and, thus, distributing ICU resources more reasonably.
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    Determining intensity of mental state of an unsound individual through text using ML
    (BRAC University, 2024-10) Khan, Nishat Sabah; Rahim, Md. Sazidur; Hossain, Muhammad Iqbal
    This research investigates the application of machine learning to detect and classify the intensity of various mental health conditions through text analysis. By analyzing user-generated statements, the study aims to identify patterns that correspond to different mental health states, such as Anxiety, Depression, Bipolar Disorder, and Suicidal tendencies. Through rigorous text preprocessing and feature extraction methods, meaningful insights are drawn from the data. The performance of the proposed approach is evaluated through standard metrics, demonstrating its potential to support mental health professionals by automating the initial stages of mental health screening. The findings highlight key challenges, such as language complexity and emotional context, and offer directions for future work to enhance the system’s accuracy and adaptability. This research provides a foundation for developing scalable, automated tools that could be integrated into mental health care and online support platforms.