Project Overview
Problem Statement:
Diabetes, a chronic metabolic disorder, affects millions worldwide and early detection is crucial.
Machine learning techniques offer a promising approach to predict diabetes based on patient-specific features.
Project Objective:
This project aims to utilize supervised machine learning algorithms, specifically the K-Nearest Neighbors
(KNN) algorithm, to build a model that effectively predicts the onset of diabetes. The model is trained and
evaluated using the Pima Indian Diabetes Dataset, a benchmark dataset for diabetes prediction.
Data Preprocessing:
The Pima Indian Diabetes Dataset underwent rigorous preprocessing to ensure data integrity and model efficiency.
Missing values were meticulously imputed with suitable strategies, and duplicated values were eliminated to maintain
data consistency. Exploratory data analysis revealed a balanced distribution of the outcome variable, indicating equal
representation of diabetic and non-diabetic patients. Outlier detection identified a few extreme values in certain
features, highlighting the need for careful data handling.
Model Training and Evaluation:
The KNN algorithm was selected for its simplicity and effectiveness in handling complex nonlinear
relationships. Hyperparameter tuning, particularly the value of k, was performed using grid search to optimize
model performance. Extensive training and testing procedures were conducted to assess the model's predictive ability.
Evaluation metrics, including accuracy, precision, recall, and F1-score, were employed to quantify the model's
performance.
Key Findings:
The KNN classifier delivered promising results in diabetes prediction. With an optimal k value of 13, the
model achieved a maximum test score of 88.89%, demonstrating its capability in accurately classifying diabetic
and non-diabetic patients. The confusion matrix provided insights into the model's prediction accuracy,
highlighting its strength in true positive and true negative classifications. The classification report further
detailed the model's performance metrics, confirming its overall effectiveness.
Conclusion:
The KNN model, optimized with k = 13, exhibited a well-balanced performance in diabetes prediction,
striking a harmonious balance between train and test scores. This model holds potential for application in
clinical settings as a supportive tool for healthcare professionals, aiding in early diabetes identification and
subsequent timely interventions.
To know more about my findings and the project, you can visit the project's Github repository by clicking the Project Link button below.