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My Ph.D. thesis finished and defended at the Brno University of Technology (2018). The package contains all necessary files such as bibtex, template, texts, figures, and all other files according to http://latex.feec.vutbr.cz
One of the most dreadful disease is breast cancer and it has a potential cause for death in women. Every year, death rate increases drastically due to breast cancer. An effective way to classify data is through classification or data mining. This becomes very handy, especially in the medical field where diagnosis and analysis are done through th…
JanSevak is a AI powered HealthCare Management System. The system allows users to register as patients, book appointments, and predict diseases based on symptoms. Doctors can view and manage appointments. Additionally, the system provides information on various health-related topics through blog posts.
NLMyo🔧: a toolbox built to leverage the power of Large Language Models (LLMs) to exploit histology text reports. Demo version at: https://lbgi.fr/NLMyo
Using a Gaussian Naive Bayes model to diagnose acute urinary inflammation and acute nephritises. Achieved a level of 90% and 95% diagnosing separately and nearly 100% with diagnosing together.
Exploring predictive K-Means Clustering, and Random Forest Classifiers in Breast Cancer diagnostics. I then work on "unboxing" the RFA to investigate feature contribution priorities - an important process in the pursuit of algorithm transparency, particularly in light of the ethical issues raised as industries shift towards complex neural algori…
Liver Disease prediction using binary classification such as SVM, ANN, or Random Forest. Generate missing data using the MICE algorithm. Use SMOTE to oversample minority class to reduce biases towards majority class. ROC analysis and k-fold Cross-validation Hypothesis tests were done. Data Source: UCI Machine Learning Repository