project_w7

🧠 Predicting Alzheimer’s Disease from Symptom Data

📌 Project Overview

This project aims to predict whether a patient has Alzheimer’s disease based on clinical and symptom data.
Alzheimer’s is a serious neurodegenerative condition, and early detection can significantly improve care and intervention strategies. Traditional diagnosis often requires expensive imaging or invasive tests.
Our goal was to build an accessible, data-driven Machine Learning model to assist in diagnosis or screening using symptom and clinical features.

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🧑‍⚕️ Project Question

Given patient symptom and clinical features, can we accurately predict the presence of Alzheimer’s disease?

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🧾 Data Sources

• Dataset: “Alzheimer’s Disease Dataset” by Rabie El Kharoua - definitions of each columns below on Demographichs Information. Source: Kaggle
• Size: 2,149 patient records
• Features: Clinical, demographic, and symptom variables (categorical & numerical)
• Target: Alzheimer’s diagnosis (binary: 0 = No, 1 = Yes)

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🧹 Data Preparation

• Dropped identifying columns (patient name, doctor name) to avoid data leakage and protect privacy.
• Checked for missing values, outliers, and inconsistencies.
• Encoded categorical variables (e.g., binary symptom flags) and scaled numerical features when necessary.
• Separated features and target, split into training and testing sets.

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🧮 Feature Engineering & Selection

To improve model interpretability and performance, we selected the top five most correlated features with the target diagnosis:

• MMSE: Mini-Mental State Examination score
• Functional Assessment
• Memory Complaints
• Behavioral Problems
• ADL: Activities of Daily Living

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🤖 Machine Learning Models

Baseline Models

Model	Accuracy	Notes
KNN	0.96	Best baseline performance
Logistic Regression	0.85	Lower recall for positive class
Decision Tree	0.94	Competitive, but less stable

KNN showed the highest accuracy, precision, and recall among baseline models.

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Ensemble Models

Model	Accuracy	Precision	Recall	Comments
Logistic Regression + Bagging	0.858	-	-
Logistic Regression + Pasting	0.958	-	-
Random Forest	0.86	0.97 (1)	0.94 (1)	Robust & easy to tune
Gradient Boosting	0.965	0.97 (1)	0.94 (1)	Best CV score
AdaBoost	0.965	0.97 (1)	0.94 (1)	Competitive

• Best Performing Models: Random Forest and Gradient Boosting both achieved 96.5% test accuracy.
• Gradient Boosting had a slightly higher cross-validation score (95.35% vs 95.17%).
• Random Forest is more robust and easier to tune for production.

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📊 Key Findings

• Clinical and symptom data can accurately predict Alzheimer’s diagnosis with ML models.
• Ensemble models (Random Forest & Gradient Boosting) outperform simple classifiers.
• MMSE and ADL scores are particularly important predictors.
• Class imbalance and dataset size may impact generalization in broader use cases.

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⚠️ Limitations & Future Work

• The dataset is relatively small and may not generalize well to all populations.
• Potential class imbalance should be addressed with resampling or weighting techniques.
• Future improvements:
  • Collect more diverse and balanced data.
  • Experiment with advanced models (e.g., Neural Networks).
  • Integrate explainable AI (XAI) methods to aid clinical interpretability.

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🧰 Tech Stack

• Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
• Models: KNN, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, AdaBoost
• Tools: Jupyter Notebook

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🛠️ How to Run

# Clone the repository

# Install dependencies

# Launch notebook for exploration and model training

📁 Repository Structure

Copy code
.
├── data/
│   ├── raw/                # Original dataset
│   └── processed/          # Cleaned data
├── notebooks/
│   ├── EDA.ipynb           # Exploratory data analysis
│   └── modeling.ipynb      # Model training and evaluation
├── src/
│   ├── train.py
│   └── utils.py
├── outputs/
│   ├── models/
│   ├── figures/
│   └── metrics/
├── presentation/
│   └── final_presentation.pdf
├── requirements.txt
└── README.md
👥 Team
    
    Julia
    Kinga
    Rafael
    Joma

Cohort: DATA-FT-Sept-2025

📜 License & Acknowledgments
Dataset from Kaggle — Rabie El Kharoua

Developed as part of DATA-FT-Sept-2025 ML Sprint

Licensed under the MIT License




Demographics Information 

Age: The age of the patients ranges from 60 to 90 years. 

Gender:   

0 = Male  

1 = Female. 

Ethnicity: The ethnicity of the patients, coded as follows: 

0: Caucasian 

1: African American 

2: Asian 

3: Other 

EducationLevel: The education level of the patients, coded as follows: 

0: None 

1: High School 

2: Bachelor's 

3: Higher 

Lifestyle Factors 

BMI: Body Mass Index of the patients 

ranging from 15 to 40. 

 
Smoking: Smoking status 

0 = No  

1 = Yes 

 
AlcoholConsumption: Weekly alcohol consumption in units 

ranging from 0 to 20. 
 

PhysicalActivity: Weekly physical activity in hours 

 ranging from 0 to 10. 


DietQuality: Diet quality score 

ranging from 0 to 10. 


SleepQuality:  Sleep quality score: 

ranging from 4 to 10. 

 
Medical History 

FamilyHistoryAlzheimers: Family history of Alzheimer's Disease 

0 = No  

1 = Yes 


CardiovascularDisease: Presence of cardiovascular disease, 

0 = No  

1 = Yes 


Diabetes: Presence of diabetes 

0 = No  

1 = Yes 


Depression: Presence of depression 

0 = No  

1 = Yes 


HeadInjury: History of head injury 

0 = No  

1 = Yes 


Hypertension: Presence of hypertension 

0 = No  

1 = Yes 

Clinical Measurements 

SystolicBP: Systolic blood pressure 

ranging from 90 to 180 mmHg. 


DiastolicBP: Diastolic blood pressure, 

ranging from 60 to 120 mmHg. 


CholesterolTotal: Total cholesterol levels 

ranging from 150 to 300 mg/dL. 


CholesterolLDL: Low-density lipoprotein cholesterol levels 

 ranging from 50 to 200 mg/dL. 

CholesterolHDL: High-density lipoprotein cholesterol levels 

ranging from 20 to 100 mg/dL. 

 
CholesterolTriglycerides: Triglycerides levels 

 ranging from 50 to 400 mg/dL. 

Cognitive and Functional Assessments 

 MMSE: Mini-Mental State Examination score 

ranging from 0 to 30.  

Lower scores indicate cognitive impairment. 


FunctionalAssessment: Functional assessment score, 

ranging from 0 to 10. 

 Lower scores indicate greater impairment. 

 
MemoryComplaints: Presence of memory complaints 

0 = No  

1 = Yes 

 
BehavioralProblems: Presence of behavioral problems 

0 = No  

1 = Yes 

 
ADL: Activities of Daily Living score 

ranging from 0 to 10.  

Lower scores indicate greater impairment. 


Symptoms 

Confusion: Presence of confusion 

0 = No  

1 = Yes 
 

Disorientation: Presence of disorientation, 

0 = No  

1 = Yes 
 

PersonalityChanges: Presence of personality changes, 

0 = No  

1 = Yes 
 

DifficultyCompletingTasks: Presence of difficulty completing tasks 

0 = No  

1 = Yes 
 

Forgetfulness: Presence of forgetfulness, 

0 = No  

1 = Yes 
 

Diagnosis Information 

Diagnosis: Diagnosis status for Alzheimer's Disease 

0 = No  

1 = Yes