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1 Predicting Sepsis Early: A Machine Learning Approach for Improved Patient Outcomes A PROJECT REVIEW REPORT Supervised by H. FAHEEM NIKHAT Assistant Professor Submitted by ANEESA BEGUM J (210171601010) MADHUNISHA M (210171601024) in partial fulfillment for the award of the degree of BACHELOR OF TECHNOLOGY in ARTIFICIAL INTELLIGENCE AND DATA SCIENCE MARCH 2025

. Cross -validation is used to make the model robust, and Explainable AI methods such as LIME are utilized to increase interpretability. By offering a clear and reliable predictive model, this research helps to enhance the management of sepsis such that early clinical interventions can be instituted and mortality rates can be decreased. 1.2 DESCRIPTION The project, titled ‘Predicting Sepsis Early: A Machine Learning Approach for Better Patient Outcomes’, is intended to use machine learning techniques for the early prediction of sepsis. The data includes 40,336 patient records, 2,932 of which are sepsis patients and 44 features

2 CHAPTER 1 INTRODUCTION Sepsis is a severe clinical syndrome that occurs when the body overreacts to infection and results in tissue damage, organ dysfunction, and death. Early recognition of sepsis at a given moment in time and early therapy greatly improves patient survival. Machine learning approaches have shown greatly promising to forecast sepsis from trending patient physiological values. The project applies sophisticated data preprocessing, feature extraction, and classification techniques in designing a solid predictive model for detecting early sepsis

. 1.2 DESCRIPTION The project, titled ‘Predicting Sepsis Early: A Machine Learning Approach for Better Patient Outcomes’, is intended to use machine learning techniques for the early prediction of sepsis. The data includes 40,336 patient records, 2,932 of which are sepsis patients and 44 features. Missing values are treated with different imputation techniques, and feature selection is done with Recursive Feature Elimination (RFE) to obtain the top 10 drivers of the data. Class

. Machine learning approaches have shown greatly promising to forecast sepsis from trending patient physiological values. The project applies sophisticated data preprocessing, feature extraction, and classification techniques in designing a solid predictive model for detecting early sepsis. In adopting Explainable AI techniques, the project realizes explainability and interpretability of models, thus enabling clinical decision-making. 1.1 OVERVIEW Sepsis remains a significant global health problem, causing millions of deaths annually. Early identification of sepsis can greatly improve patient outcomes and avoid serious complications

. The result of the project can be implemented in clinical de cision support systems in a bid to decrease sepsis mortality and enhance patient outcomes. 1.5 ORGANIZATION OF THE PROJECT REPORT Chapter 1: Introduction – Gives an overview, description, objectives, and organization of the report. Chapter 2: Literature Review – Explains current research on sepsis prediction, machine learning methods, and related work. Chapter 3: Methodology – Explains the dataset, preprocessing methods, feature selection methods, machine learning models, and evaluation metrics employed in the study.

14 CHAPTER 4 SYSTEM METHODOLOGIES The approach adopts a systematic methodology in forecasting early sepsis through machine learning methods. The procedure entails some of the most important steps, such as preprocessing data, feature selection, handling class imbalance, training and validation of models, and explanation analysis. This provides guarantees that the model is interpretable, stable, and efficient in real data. 4.1 MODULE EXPLANATIONS The system is divided into several modules, and each module processes a portion of the sepsis prediction pipeline. All the modules collaborate to preprocess and clean data, feature selection, train models, and make predictions

. Based on an analysis of the physiological parameters of patients, we develop an effective predictive model that detects possible instances of sepsis before they become severe conditions. Explainable AI methods will be employed to facilitate medical experts' interpretation of model prediction such that they make confident decisions. The project utilizes high-level data preprocessing methods, feature selection techniques, and high-level machine learning algorithms to achieve increased prediction accuracy. The result of the project can be implemented in clinical de cision support systems in a bid to decrease sepsis mortality and enhance patient outcomes

Question: what are final features selected in the "Predicting Sepsis Early: A Machine Learning Approach for 
Improved Patient Outcomes" paper

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