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The workflow includes data preprocessing, feature selection via a correlation matrix, hyperparameter tuning with cross-validation, and class-imbalance handling through SMOTE. Model interpretability is provided with SHAP to identify feature contributions and decision drivers. A Streamlit-based interface is designed for real-time provider-facing predictions and explainable 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|\n\n2025  \nMachine Learning and SHAP Interpretability for Chronic Disease Understanding  \nNnaemeka Charles Igwe  \nFollow this and additional works at: [https://scholar.dsu.edu/erposters](https://scholar.dsu.edu/erposters)  \n\n| Machine Learning and SHAP Interpretability for Chronic Disease Understanding\u003Cbr>Nnaemeka Charles Igwe, Khandaker Mamun Ahmed\u003Cbr>DAKOTA STATE UNIVERSITY |  |\n| --- | --- |\n\nIntroduction  \n• Non-communicable diseases (NCDs), such as diabetes, are major global health concerns influenced by various health parameters and lifestyle choices.  \n• Traditional methods struggle to efficiently predict and manage these conditions due to the complexity and diversity of medical data.  \n• There is a need to leverage machine learning algorithms and modern computational tools to accurately predict diabetes, improve diagnosis, and provide actionable insights for better healthcare outcomes.  \n• There are various health parameters and lifestyle choices responsible for diabetes.  \n• Key health parameters include age, insulin level, body mass index (BMI), and family history.  \nProblem Statement  \n• Our study predicts non-communicable diseases (NCDs), such as diabetes, by leveraging machine learning algorithms.  \n• We leverage hyperparameter tuning techniques for model development and SHapley Additive exPlanation (SHAP) for results interpretations.  \nProject Goals  \n• Train machine learning models for diabetes prediction  \n• Utilize SHAP for feature importance and model interpretability.  \n• Handle class imbalance using SMOTE.  \n• Identify key factors affecting diabetes risk.  \n• Develop a user-friendly web interface for healthcare providers using STREAMLIT.  \nSpecifications  \n• Data: We use the Pima Indian dataset that includes raw (diabetes.csv) and processed datasets (updated_diabetes_dataset.csv) and SMOTE to handle class imbalance.  \n• Key Tools: Python for scripting, Streamlit for building a web app interface, and SHAP for explaining black box AI model predictions.  \nMethodology  \n• Data Preparation: We use the PIMA Indian diabetes dataset, preprocess data and select feature using a correlation matrix.  \n• Model Training and Evaluation: We train different machine learning models (SVM, KNN, Logistic Regression(LR)), fine-tune the hyperparameters, perform cross-validation, and evaluate the models’ performance using metrics like accuracy, precision, recall, and F1-score.  \n• Deployment and Interpretability: We deploy a Streamlit-based web app for real-time predictions and utilize SHAP to explain feature importance and model decisions.  \n•  \n•  \n•  \n•  \nFig 1: The overall architecture of our proposed method  \nResults  \nFig 3. F-1 score, Recall, Accuracy, Precision of SVM, KNN, and LR models  \nFig 5: Individual feature’s contribution to the prediction.  \nFig 4: SHAP interpretation of features importance in predicting diabetes using the SVM model  \nFig 6: Compares the performance of LinearSVC, KNN, and LR after applying SMOTE  \n•  \n•  \n•  \n•  \n•  \n•  \n Discussion  \nFigure 2 and 3 show the comparison of SVM, KNN, and LR models shows that SVM and KNN consistently outperform LR in key metrics like accuracy, precision, recall, and F1-score.  \nSVM displayed the most robust performance overall, while KNN was competitive in precision and recall. LR, although less effective, demonstrated acceptable results in simpler scenarios.  \nGlucose is the most influential feature, with higher values strongly contributing to a positive diabetes prediction. Age and BMI are also significant predictors, where higher values generally indicate an increased risk. The plot visually distinguishes high (pink) and low (blue) feature values and their corresponding SHAP values, showing how individual features influence the model's predictions.  \nLinearSVC performs the best, with the highest Accuracy (0 .76) and F1-Score (0 .63) .  \nConclusion  \nThis ","cbCaikecibSrV4y7","https://ap.wps.com/l/cbCaikecibSrV4y7","pdf",347873,"English","# Introduction\n# Problem Statement\n# Project Goals\n# Specifications\n# Methodology\n## Data Preparation\n## Model Training and Evaluation\n## Deployment and Interpretability\n# Results\n# Discussion\n# Conclusion\n# Reference","[{\"question\":\"What machine learning task does the project focus on?\",\"answer\":\"The project predicts non-communicable diseases, specifically diabetes, using machine learning models trained on the Pima Indian dataset.\"},{\"question\":\"How is model interpretability achieved in the study?\",\"answer\":\"SHAP (SHapley Additive exPlanation) is used to explain feature importance and individual feature contributions to model predictions.\"},{\"question\":\"Which models are trained and how are they evaluated?\",\"answer\":\"SVM, KNN, and Logistic Regression models are trained with hyperparameter tuning and cross-validation, evaluated using accuracy, precision, recall, and F1-score.\"}]","Machine Learning and SHAP Interpretability for Chronic Disease Understanding - Poster | PDF"]