Vertebral Column Data Set Analysis Report

Executive Summary

This report outlines the analysis conducted on the Vertebral Column Data Set, with the aim of distinguishing between normal and abnormal spinal conditions using machine learning techniques. The K-nearest neighbors (KNN) algorithm was employed, alongside a variety of distance metrics, to make predictions based on biomechanical attributes. The analysis yielded significant insights into the effectiveness of different metrics and showcased the potential of machine learning within biomedical applications.

Introduction

The Vertebral Column Data Set, curated by Dr. Henrique da Mota, includes biomechanical attributes that characterize the shape and orientation of the pelvis and lumbar spine. The objective of this project was to apply and assess machine learning techniques for binary classification tasks, predicting normal versus abnormal spinal conditions, and contributing to medical diagnostic processes.

Data Preprocessing and Exploratory Analysis

Preprocessing Steps:

• Cleaning: The dataset was cleaned to eliminate any inconsistencies or missing values.
• Normalization: Attributes were normalized to ensure a uniform scale across the dataset.

Exploratory Data Analysis:

• Scatterplots and boxplots were created for each biomechanical attribute, employing color coding to differentiate between the two classes (normal and abnormal), to visualize data distributions and identify outliers.

Methodology

Classification with KNN:

• The KNN algorithm was selected for its simplicity and was applied using the Euclidean metric. The dataset was segmented into a training set of 210 observations and a test set comprising the remaining observations.

Parameter Tuning:

• A range of k values was evaluated to determine the optimal balance between overfitting and underfitting, starting from 208 and decrementing in steps.

Distance Metrics Exploration:

• We extended our analysis beyond the Euclidean metric to include Minkowski, Manhattan, Chebyshev, and Mahalanobis distances.

Results

Optimal k Value:

• The optimal k value was determined through an iterative testing process, with a focus on minimizing the test error rate.

Distance Metrics Comparison:

• The performance of each distance metric was compared using the entire training data set, identifying the most effective metric in each scenario.

Learning Curve:

• A learning curve was plotted, correlating the size of the training set with the best test error rate, providing insights into training size optimization for model accuracy.

Conclusion

The project demonstrated the successful application of the KNN algorithm to the Vertebral Column Data Set, emphasizing the value of tailored approaches in machine learning. The importance of data preprocessing, exploratory analysis, and meticulous parameter tuning was highlighted, underpinning their significance in developing accurate and reliable predictive models.