Welcome to the Predicting Bone Fractures with Machine Learning project repository! This project aims to investigate the relationship between patient risk factors and the likelihood of experiencing bone fractures using advanced machine learning techniques.
The primary aim of this project is to utilize machine learning models to analyze patient data and accurately predict the likelihood of bone fractures based on various risk factors. By identifying which risk factors are most strongly associated with bone fractures, I aim to develop accurate predictive models that can assist healthcare professionals in identifying individuals at high risk of bone fractures. This, in turn, will enable the development of targeted interventions to prevent or mitigate fracture risk.
Null Hypothesis: Factors such as age, gender, bone mineral density, BMI, and comorbidities like diabetes, coronary artery diseases, chronic kidney disease, and chronic obstructive pulmonary disease do not influence bone fractures.
Alternative Hypothesis: The factors such as age, gender, bone mineral density, and comorbidities like diabetes, coronary artery diseases, chronic kidney disease, and chronic obstructive pulmonary disease influence bone fractures.
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Data Collection: Obtaining relevant datasets containing information on risk factors contributing to bone fractures.
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Data Extraction, Cleaning, and Storage: Preprocessing and organizing the collected data to prepare it for analysis.
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Data Analysis: Analyzing the cleaned data to identify patterns and relationships between risk factors and bone fractures.
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Data Visualization and Machine Learning Models: Visualizing the analyzed data and developing machine learning models to predict the likelihood of bone fractures based on identified risk factors.
