Harnessing Machine Learning to Predict Diabetes: A Data-Driven Approach
Introduction:
In today’s data-rich world, the field of healthcare is undergoing a transformative shift with the help of machine learning. One compelling application of this technology is the prediction of diabetes. We have created a strong model that shows promise in accurately categorising people as diabetic or not by utilising a broad dataset and cutting-edge algorithms. We will go into great detail about our project in this blog post, examining the dataset, looking for trends, and outlining the procedures for developing and testing the prediction model.
Understanding the Dataset:
Our dataset, which has 100,000 records and 9 columns, offers a thorough analysis of the numerous risk factors for diabetes. Gender, age, blood pressure, heart disease, smoking history, BMI, HbA1c level, blood sugar level, and diabetes status are some of these factors. We learn important things about the connections between these qualities and how they affect the prevalence of diabetes by analyzing this dataset.
Exploratory Data Analysis: Unveiling the Patterns
We started an exploratory data analysis trip in order to comprehend the dataset more thoroughly. We discovered fascinating patterns and correlations through visualisations, and these findings guided our project-wide decision-making. We examined the age distribution, analyzed the impact of smoking history on diabetes prevalence, and investigated the relationships between various attributes using correlation heatmaps. These visualizations provided a solid foundation for our subsequent analysis and model development.
Building the Machine Learning Model
With a comprehensive understanding of the dataset, we began building our prediction model. Our workflow encompassed several critical steps:
1. Preprocessing: We performed data cleaning tasks, removing duplicated values to ensure the integrity of the dataset. Additionally, we applied one-hot encoding to categorical variables like gender and smoking history, enabling us to utilize them effectively in the prediction process.
2. Feature Engineering: We concatenated the encoded columns with other relevant features, such as age, hypertension, heart disease, BMI, HbA1c level, and blood glucose level. This process enabled us to create a comprehensive set of features that would aid in accurate diabetes prediction.
3. Data Normalization: To ensure consistency and comparability across features, we employed MinMaxScaler to normalize the data, scaling the values within a specific range. This step eliminated any biases caused by differences in the magnitude of the features.
4. Addressing Class Imbalance: In our dataset, we encountered class imbalance, with a significantly higher number of non-diabetic cases compared to diabetic cases. To mitigate this issue, we employed the Synthetic Minority Over-sampling Technique (SMOTE), which intelligently generates synthetic samples of the minority class. This approach helped us balance the dataset and improve the model’s ability to accurately classify both diabetic and non-diabetic individuals.
5. Algorithm Selection and Hyperparameter Tuning: We evaluated multiple algorithms, including logistic regression, decision tree classifier, random forest classifier, and more. Each algorithm was fine-tuned by exploring different hyperparameter combinations to identify the optimal configuration for our specific prediction task.
Model Evaluation and Performance
After training and testing our models, we thoroughly evaluated their performance. Using various evaluation metrics, including confusion matrices and classification reports, we gained insights into each model’s strengths and weaknesses. We assessed accuracy, precision, recall, and F1-score to comprehensively measure the model’s predictive capabilities. This rigorous evaluation allowed us to select the most effective model for diabetes prediction.
Dimensionality Reduction with PCA
As a final step, we applied Principal Component Analysis (PCA) to reduce the dimensionality of our dataset while retaining essential information. This technique helped us simplify the model without sacrificing accuracy. By reducing complexity, we improved efficiency and interpretability, making our model more practical and adaptable.
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Github link: https://github.com/vikaschauhan734/diabetes_prediction
LinkedIn Profile: https://www.linkedin.com/in/vikas-chauhan-700a7b189/
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