Faculty of Science and Engineering

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Start date: 2020Subject: search.filters.subject.Random Forest

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    Dengue detection using Machine Learning Algorithms
    (International Islamic University Chittagong, 2022-07) Turhan, Nazmus Sakib; Hossain, Md. Hasibul; Hossain, Md. Reyad
    The dengue virus, a global health concern with an estimated 400 million annual infec tions, has posed a critical challenge worldwide. Between January 1 and August 7, 2023, Bangladesh experienced a concerning surge in dengue cases, reporting over 69,000 con firmed cases and 327 deaths according to the Ministry of Health and Family Welfare. Timely and accurate diagnosis is necessary for intervention and treatment optimization, possibly relieving severe complications and saving lives. In response, this paper intro duces a machine-learning approach to predict outbreaks in Chittagong and Cox’s Bazar. Through careful analysis of region-specific survey data, we identified local triggers for dengue spikes. Our data-driven model empowers healthcare officials with predictive ca pabilities to address future outbreaks, safeguarding communities. The study featuresa unique real-time dataset collected from healthcare institutions across Chittagong and Cox’s Bazar, involving collaboration with esteemed public and private institutions. By using diverse data, the dataset aims to unveil hidden insights into dengue outbreaks, guiding accurate predictions and effective prevention measures. The comprehensive pa tient dataset, including diagnoses, medical history, and symptoms, underwent precise model training with a 70:30 split. we have applied various machine learning algorithms, namely - SVM, Decision Tree, XGBoost, Naive Bayes, Random Forest, K-NN, Logistic Regression, and LDA. The Random Forest demonstrated accuracy of 98.92 percent on both training and test data. Performance assessment included a confusion matrixand macro averages for precision, recall, and F1-measure scores.
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    Cancer prognosis & Survival rate prediction using machine learning algorithm.
    (Department of Electronic and Telecommunication Engineering, 2022-10) Alif, Borhan Uddin
    Roughly 10 million deaths, or nearly one in six deaths, were caused by cancer in 2020, making it the top cause of death globally. Breast, lung, colon, rectum, and prostate cancers are the most prevalent types of cancer. Any disease that can affect any region of the body is referred to as cancer. Neoplasms and malignant tumors are other words that are used. One characteristic of cancer is the quick development of aberrant cells that expand outside of their normal borders, infiltrate other body components, and eventually move to other organs. This process is known as metastasis. The main reason why cancer patients die is because of widespread metastases [1]. The research was held on two different datasets of breast cancer. To train our model initially we used a primary data set from UCI which includes 569 instances with several attributes [2]. This was for cancer diagnosis prediction/detection. Then we used another dataset that includes 4024 instances with 14 different attributes which we mainly used for death rate prediction with total analysis [3]. Here 6 algorithms were incorporated including KNN, Random Forest, J48, etc., and their results were compared.