Thema09-2023

WekaRunner

Introduction

Welcome to the WekaRunner project! This Java application utilizes the Weka library for machine learning to classify patients into "Demented" or "Non-demented" categories based on MMSE scores. The project consists of a command-line interface for both analyzing datasets and making predictions for single instances.

Prerequisites

Make sure you have the following installed on your system:

• Java (JRE or JDK)
• Weka library

Usage

Analyzing a Dataset

To analyze a dataset and get the number of "Demented" and "Non-demented" cases. This file must include the following attributes: MMSE score and Group (that can be empty). Use the following command:

java -jar WekaAplicatie-1.0-SNAPSHOT-all.jar -f <path-to-dataset-file>

Ensure that the dataset file has a '.arff' extension.

Making Predictions for Single Instance

To make predictions for a single instance based on the MMSE score, use the following command:

java -jar WekaAplicatie-1.0-SNAPSHOT-all.jar -v <MMSE-score>

Make sure to replace <MMSE-score> with the actual MMSE score you want to predict.

Options

• -f, --file: Analyze a dataset. Provide the path to the dataset file.

• -v, --value: Make predictions for a single instance. Provide the MMSE score.

Examples

Analyzing a Dataset

java -jar WekaAplicatie-1.0-SNAPSHOT-all.jar -f DataGuess.arff

Making Predictions for Single Instance

java -jar WekaAplicatie-1.0-SNAPSHOT-all.jar -v 28

Important Notes

• Ensure that the dataset follows the '.arff' format.
• The MMSE score must be a numeric value between 1 and 30.

RStudio_files

Thema09DementiaPrediction.docx is the document were u will find the EDA steps of this project for cleaning the dataset.

Support

for any reported issues or help: e.a.ooms@st.hanze.nl