The Data Science Hackathon is an exciting event designed to satisfy the intense desire of those who, like me, find fascination in exploring innovative ideas and creating technical solutions based on data analysis. This stimulating competition takes place in the context of the prestigious International Energy and Expotech Symposium, bringing together a diverse group of data professionals, engineers, analysts and ardent technology enthusiasts, from all corners of the world.
What really sets this hackathon apart is its technical and rigorous focus on collaboration and shared learning. Participants are grouped into multidisciplinary teams, allowing me to merge my technical data science skills with the specialized knowledge of other experts. Additionally, the event has valuable guidance from experienced mentors who not only raise the bar of skills but also provide critical perspective in solving complex challenges.
In this hackathon, we tackle real-world problems that require a solid understanding of advanced data analysis, machine learning, and visualization techniques. Participants have the opportunity to work with challenging data sets, explore predictive modeling strategies, and use cutting-edge tools to come up with innovative solutions. In addition, technical issues such as the fusion of data from various sources, algorithm optimization and data quality management are addressed, adding an additional level of complexity and technical depth to the event. In short, the Data Science Hackathon is an exceptional opportunity to elevate our technical skills, collaborate with passionate colleagues, and apply our knowledge in a real and challenging context.
Context: Health is a fundamental aspect of people's lives. Given the amount of information generated from patients, data science can play a crucial role in early identification, prevention, monitoring, and prediction of medically relevant diseases (diabetes, cancer, etc.).
Objective: This challenge involves using different data sources to develop analysis, detection, prevention, and/or monitoring models of diseases based on historical patient health data.
Data Sources (including but not limited to):
- Etiological factors of renal failure
- Risk of cardiovascular diseases
- Early prediction of diabetes
- Lung cancer detection
- Breast cancer prognosis
This code is a crucial part of an ambitious data science project, primarily focused on the in-depth analysis of extensive datasets related to cardiovascular diseases. The project encompasses multiple objectives, including robust data preprocessing, the strategic development and deployment of machine learning models, and the meticulous evaluation of their performance metrics.
One of the central components of this project is the implementation of a screening model. This model functions as a web-based questionnaire designed to systematically identify potential risk factors associated with cardiovascular diseases. If an individual is identified as having an elevated risk of cardiovascular disease, the model seamlessly facilitates the scheduling of a medical appointment to conduct further assessments and diagnosis.
Furthermore, the screening model grapples with challenges such as dealing with an imbalanced database and rudimentary data that can predict risk but not provide a comprehensive diagnosis and prediction of cardiovascular diseases. To address this, two additional models have been implemented: a diagnostic model and a classification model for cardiac arrhythmias. These models allow medical professionals to input patient information, and based on the training data, make informed decisions regarding the presence of a cardiovascular disease.
This holistic approach not only streamlines traditional, resource-intensive processes but also enhances the overall patient care system, optimizes resource allocation, and ultimately leads to an improved patient experience.