Patient Survival Predictive Analysis

Accuracy Performance Benchmarking

Inspiration

The growing number of patients in hospitals as well as the increasing complexity of their medical data inspired us to develop a solution that could predict patient outcomes. Thus, we believed that by using machine learning, we could assist medical professionals in making informed decisions and potentially save lives.

What it does

Our program predicts whether a patient will survive or not based on multiple medical features provided in datasheet. It uses machine learning models to make these predictions. The program also preprocesses the data, handles missing values, and selects the most relevant features to improve prediction accuracy.

How we built it

We started by loading and preprocessing the data, ensuring that missing values were handled appropriately. We then encoded categorical data to make it suitable for machine learning models. To ensure that our models were trained on the most relevant features, we calculated the correlation of each feature with the target variable and selected those with a high correlation. We then trained multiple models, including a neural network, random forest, SVM, and logistic regression, and evaluated their performance.

Challenges we ran into

Handling missing data was a significant challenge, as we had to decide whether to drop rows with missing values or fill them. We also faced challenges in selecting the most relevant features for training our models and ensuring that the data was in the right format for each model.

Accomplishments that we're proud of

We successfully integrated machine learning model into our program. Our program can now predict patient outcomes with a high degree of accuracy. Before this, I did not have any experience or any knowledge about dealing with machine learning model, so finishing our product is already such an accomplishment.

What we learned

We learned the importance of data preprocessing and feature selection in machine learning. We also gained experience in training and machine learning models and understanding their strengths and weaknesses during this competition.

What's next for Patient Survival Predictive Analysis

We plan to further refine our models by incorporating more features and using more advanced algorithms. We also aim to integrate our program with electronic health record systems, allowing medical professionals to use it in real-time to make informed decisions about patient care.