SACHI MONNAPPA

1.Implementation of Direct Sequence Spread Spectrum Modulator on FGPA Board(2021):

Developed a MATLAB code for simulations of 4 stage feedback shift register and led my team in successfully analyzing the performance parameters of direct sequence spread spectrum system. 

2.Nursing Home Quality: Insights and Recommendation(2024):

• Conducted an in-depth analysis of nursing home care standards across multiple states, focusing on ownership, certification, and staffing levels to assess their impact on overall quality ratings.
• Preprocessed data by handling missing values, selecting significant features, and performing train-test splits to ensure model robustness. Built predictive models, with Gradient Boosting achieving the highest R² score (0.70).
• Found a positive correlation between RN staffing and quality, leading to recommendations for staffing and certification improvements.

3.Acute Kidney Injury Progression Prediction(2024) 

• Developing a machine learning model to predict the progression of Acute Kidney Injury (AKI) from early stages (1/2) to advanced stage 3 in critically ill patients using the MIMIC-III dataset.
• Leveraging Logistic Regression and XGBoost algorithms to analyze critical risk factors such as serum creatinine levels, multiple organ failure syndromes (MODS), and vital signs.
• Implementing advanced data preprocessing techniques, including handling missing values and balancing datasets, to enhance model robustness.
• Achieved superior predictive performance with XGBoost, significantly outperforming traditional logistic regression models in terms of precision and recall metrics.
• Designing a real-time clinical decision support tool aimed at early intervention, reducing the risk of mortality and chronic kidney disease progression in high-risk patients.