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I have developed a Streamlit application as part of my medical thesis, where I have devised a novel anthropological method for predicting age at the time of death in subjects aged over 50 years. This prediction is accomplished using machine learning models.

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DrKenani/eledery_age_app

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eledery_age_app

Hello everyone, my name is Mohamed Kenani, and I am a medical resident. This is my medical thesis project titled: "ESTIMATION OF AGE AT DEATH IN SUBJECTS OVER 50 YEARS OLD: AN INNOVATIVE ANTHROPOLOGICAL APPROACH." I have developed a novel anthropological method that combines medicine and machine learning using Python and ScikitLearn. I have also created a web application with Streamlit to apply this work. You will find the datasets and code in the 'eledery_age_notebook.ipynb'. It contains all the statistical analysis, model creation, and a brief explanation of the work. In 'main.py', you will find all the code for creating the web application. In the 'models' folder, you will find all the models created in joblib format. Finally, you can visit our web application at this link: https://eldery-age-app.streamlit.app/ I hope that this work can be of assistance to you, and please feel free to contact me for any remarks or questions.

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I have developed a Streamlit application as part of my medical thesis, where I have devised a novel anthropological method for predicting age at the time of death in subjects aged over 50 years. This prediction is accomplished using machine learning models.

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