Multi-Class Classification Model for Egyptian HCV Patients Using Python Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Egyptian HCV Patients dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset captured the Egyptian patients who underwent treatment dosages for HCV for about 18 months. The goal is to predict the patient’s condition, in stages, based on the available measurements.

In this iteration, we will construct machine learning models with some simple and straight-forward data preparation steps. This model will serve as the baseline for the future iterations of modeling.

ANALYSIS: The baseline performance of the machine learning algorithms achieved an average accuracy of 25.85%. Two algorithms (k-Nearest Neighbors and Random Forest) achieved the top accuracy metrics after the first round of modeling. After a series of tuning trials, k-Nearest Neighbors turned in the top overall result and achieved an accuracy metric of 29.48%. By using the optimized parameters, the Bagged Decision Trees algorithm processed the testing dataset with an accuracy of 24.78%, which was no better than the prediction from the training data.

CONCLUSION: For this iteration, the k-Nearest Neighbors algorithm achieved the best overall results using the training and test datasets, but all algorithms performed poorly. For this dataset, we should consider applying more feature engineering techniques to the dataset before performing further modeling.

Dataset Used: Hepatitis C Virus (HCV) for Egyptian patients Data Set

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository []. Irvine, CA: University of California, School of Information and Computer Science.

The HTML formatted report can be found here on GitHub.