Alimardan-ai/Malaria-Diagnostic-AI

AI triage tool that predicts malaria risk from clinical symptoms using Random Forest. Built for low-resource healthcare environments. # 🦟 AI-Driven Malaria Diagnostic Support System > A Machine Learning approach to healthcare accessibility in low-resource environments. --- ## 📌 Motivation In many regions of Central Asia and Africa, access to professional medical diagnostics is extremely limited. A patient may wait days before receiving a blood test. This project explores whether a simple, lightweight Machine Learning model can help health workers **prioritize high-risk patients based on symptoms alone** — before a lab test is available. The goal is not to replace doctors, but to support triage decisions in resource-constrained environments. --- ## 🧠 How It Works ``` Patient symptoms (binary: yes/no) │ ▼ Data Preprocessing ──► Handle missing values (Imputation) │ ▼ Random Forest Classifier ──► Trained on 800 patient records │ ▼ Risk Probability (0–100%) ──► LOW / MODERATE / HIGH │ ▼ Triage Recommendation ``` --- ## ⚙️ Technical Stack | Component | Detail | |-----------|--------| | **Algorithm** | Random Forest Classifier | | **Why RF?** | Works well with tabular/binary data, robust to noise in medical records | | **Imbalance handling** | `class_weight='balanced'` — prevents model from predicting "Healthy" for everyone | | **Missing values** | Median imputation — realistic for field conditions | | **Libraries** | Python, Scikit-learn, Pandas, NumPy | --- ## 📊 Features (Symptoms) | Feature | Type | |---------|------| | Fever | Binary (yes/no) | | Chills | Binary (yes/no) | | Sweating | Binary (yes/no) | | Headache | Binary (yes/no) | | Vomiting | Binary (yes/no) | | Anaemia | Binary (yes/no) | | Splenomegaly | Binary (yes/no) | | Jaundice | Binary (yes/no) | | Fatigue | Binary (yes/no) | | Muscle Pain | Binary (yes/no) | | Rapid Breathing | Binary (yes/no) | | Age | Integer | | Body Temperature (°C) | Float | --- ## 📈 Model Performance - **Accuracy:** ~87% - **Recall:** ~89% ← most important metric in medicine (don't miss sick patients) - **Key insight:** Fever, splenomega …