Machine Learning Practice for Predicting Alzheimer's Disease Using KNN and Decision Tree Algorithms

Title: Machine Learning Practice for Predicting Alzheimer's Disease Using KNN and Decision Tree Algorithms

Core Viewpoint: This project is based on supervised learning, using K-Nearest Neighbors (KNN) and Decision Tree algorithms to classify and predict Alzheimer's disease patient data, covering the complete processes of data processing, model training, and evaluation. The project is maintained by ariapatrikaki and published on GitHub on 2026-05-30. Original link: https://github.com/ariapatrikaki/Machine-Learning

Keywords: Machine Learning, Medical Diagnosis, Alzheimer's Disease, K-Nearest Neighbors, Decision Tree, Supervised Learning, Python, scikit-learn

Alzheimer's disease is the most common cause of dementia in the elderly. The intensification of global aging makes early diagnosis crucial. Traditional diagnosis relies on clinicians' experience, which has problems such as strong subjectivity and difficulty in early identification. Machine learning technology analyzes patients' clinical data to learn the relationship between disease features and diagnosis results, helping doctors make more accurate judgments. This project explores the application of classic supervised learning algorithms in the predictive diagnosis of this disease.

Technical Solution

Two classic supervised learning algorithms are used for comparison:

1. KNN: Instance-based learning, core is neighbor voting. Advantages include intuitive understanding, no training phase needed, and non-parametric nature suitable for complex medical data.
2. Decision Tree: Recursively divides features to build a tree model. Features include strong interpretability, automatic feature selection, and handling mixed data.

Workflow

1. Data Preprocessing: Cleaning, standardization, handling missing values, etc.
2. Model Training: Cross-validation to avoid overfitting.
3. Hyperparameter Tuning: Optimize K value for KNN, maximum depth for Decision Tree, etc. (Grid/Random Search).
4. Evaluation: Use metrics such as accuracy, precision, recall, F1 score, confusion matrix, etc.
5. Model Comparison: Analyze the advantages and disadvantages of the two algorithms.

The repository contains two Notebook files:

• DecisionTree_Assigememt_patrikaki.ipynb: Complete implementation and evaluation of the Decision Tree model
• KNN_patrikaki.ipynb: Complete implementation and evaluation of the KNN algorithm

The separate design facilitates independent learning, reproduction, and comparative analysis.

The project demonstrates a typical application mode of medical AI:

1. Problem-driven: Starting from clinical needs
2. Method comparison: Not superstitious about a single algorithm
3. Full process coverage: Cultivate engineering thinking
4. Interpretability first: Choose models that doctors can understand

It is a good reference for entry-level medical AI developers, proving that with standardized processes and rigorous evaluation, simple algorithms can also produce valuable diagnostic auxiliary tools.