An AI-Powered Integrated Care Model Combining Traditional Chinese Medicine and Western Medicine for Personalized Alzheimer’s Disease Management

Abstract

Alzheimer's Disease (AD) gradually reduces memory and cognition. While Western medicine focuses on pharmacological treatments, it frequently ignores holistic factors such as mood and digestion. Traditional Chinese Medicine (TCM) addresses these issues through a wellness-oriented approach. However, the combination of both systems in patient care is uncommon. Current Alzheimer's disease treatments are often siloed, relying solely on Western or alternative medicine. A unified, intelligent system is required to capitalize on the advantages of both methods.  This study aims to create an AI-powered model that combines TCM and Western medicine to offer personalized Alzheimer's care, allowing for early diagnosis and improved treatment plans that contain herbs, drugs, and lifestyle recommendations. The proposed AI-Powered Integrated Alzheimer's Care (AIP-IAC) model is based on patient data, including demographics, imaging, lab results, cognitive scores, and treatment history. After preprocessing the patient data, machine learning models such as Random Forest and XGBoost are employed to generate predictions, while clustering algorithms discover unique patient subgroups using similar features. A Long Short-Term Memory (LSTM) tracks symptom progression, and a rule-based TCM engine improves AI recommendations into personalized plans, which are continuously updated using patient feedback. AIP-IAC was tested on 1,200 patients and achieved 91.3% accuracy, with high precision (89.7%), recall (90.2%), and F1-score (89.9%). Over six months, it decreased trial-and-error by 32% while increasing patient quality-of-life metrics like sleep and mood by 25-30%.  Incorporating TCM and Western practices using AI results in more personalized and efficient Alzheimer's care. The AIP-IAC model enhances early detection, treatment precision, and patient well-being