Diabetes Risk Prediction Using Machine Learning

Project Overview

Diabetes Mellitus is one of the most critical health concerns worldwide, often leading to severe complications such as heart disease, kidney damage, and nerve disorders. This project leverages machine learning techniques to predict the risk of diabetes using clinical and historical data.

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Objectives

Early Detection

• Identify at-risk individuals by analyzing key factors and symptoms.
• Enable timely intervention to reduce complications.

Healthcare Cost Reduction

• Minimize treatment costs by diagnosing diabetes at an early stage.
• Avoid long-term complications through preventive measures.

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Problem Statement

Diabetes is caused by a group of metabolic disorders that lead to elevated blood sugar levels. If untreated, it can result in severe health risks. This project overcomes current diagnostic limitations using machine learning to analyze health data and predict diabetes risks effectively.

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System Workflow

1. Data Collection & Preprocessing
   • Gather and clean clinical and historical data.
2. Feature Engineering
   • Select relevant features for accurate predictions.
3. Model Training
   • Train models like Logistic Regression, Random Forest, and Decision Trees.
4. Prediction
   • Use trained models to predict diabetes risk.
5. Model Updates
   • Regularly update models with new data to improve accuracy.

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Challenges in Existing Systems

1. Dependency on historical data, which may not always reflect current trends.
2. Bias in training datasets leading to incorrect predictions.
3. Adapting to dynamic lifestyle changes like diet and exercise.
4. Need for frequent updates to remain relevant.

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Literature References

1. Jaiswal, V., et al. (2021). A review on current advances in machine learning-based diabetes prediction.
2. Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms.
3. Srivastava, S., et al. (2019). Prediction of diabetes using artificial neural network approach.
4. Hasan, M. K., et al. (2020). Diabetes prediction using ensembling of different ML classifiers.
5. Liu, C., et al. (2018). Progressive neural architecture search.
6. Bukhari, M. M., et al. (2021). An improved ANN model for diabetes prediction.
7. Maniruzzaman, M., et al. (2020). Classification of diabetes disease using ML paradigms.
8. Kalyankar, G. D., et al. (2017). Predictive analysis of diabetic data using Hadoop.
9. Anand, A., & Shakti, D. (2015). Prediction of diabetes based on lifestyle indicators.
10. Eswari, T., et al. (2015). Predictive methodology for diabetic data analysis.

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