Anemia Detection Using Machine Learning: A Comprehensive Guide with Code

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Introduction

Anemia is a common medical condition characterized by a lack of healthy red blood cells or hemoglobin, leading to reduced oxygen transport in the body.

Traditional diagnosis requires blood tests, but recent advancements in machine learning for anemia detection have introduced non-invasive, automated diagnostic tools.

This blog explores how AI can detect anemia, provides step-by-step implementation, and links to top GitHub projects for hands-on learning.

anemia detection using machine learning github
Anemia detection using machine learning GitHub
Anemia detection using image processing
Anemia dataset
Anemia eye dataset
Non invasive anemia detection
Anemia detection and classification from blood samples using data analysis and deep learning
An intelligent non invasive system for automated diagnosis of anemia exploiting a novel dataset
Anemia detection using palpable palm image datasets from Ghana

What is Anemia Detection Using Machine Learning?

Machine learning can diagnose anemia using different methods:

• Blood Test Analysis – Predicts anemia based on hemoglobin levels, red blood cell count, and mean corpuscular hemoglobin (MCH).
• Image Processing – Uses deep learning to analyze blood smear images or conjunctiva images (inner eyelid) for anemia indicators.
• Non-Invasive Detection – Predicts hemoglobin levels using smartphone images or wearable sensors.

By leveraging classification algorithms (Random Forest, SVM, Logistic Regression) and deep learning architectures (CNNs, LSTMs), machine learning can provide accurate anemia detection without invasive tests.

Step-by-Step Guide to Implementing Anemia Detection

1. Data Collection & Preprocessing

To build a machine learning model for anemia detection, we need a dataset containing blood-related attributes. Kaggle and UCI Machine Learning Repository offer excellent medical datasets.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

data = pd.read_csv("anemia_dataset.csv")
X = data.drop(columns=['Anemia_Status'])  # Features
y = data['Anemia_Status']  # Target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

2. Building a Machine Learning Model

A Random Forest Classifier can effectively predict anemia.

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy * 100:.2f}%")

3. Deep Learning for Anemia Detection from Images

A Convolutional Neural Network (CNN) can classify blood smear or conjunctiva images for anemia detection.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential([
    Conv2D(32, (3,3), activation='relu', input_shape=(128,128,3)),
    MaxPooling2D(2,2),
    Conv2D(64, (3,3), activation='relu'),
    MaxPooling2D(2,2),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(1, activation='sigmoid')  # Binary classification
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()

4. Training & Evaluating the Model

history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

Best GitHub Projects for Anemia Detection Using Machine Learning

Here are some of the best GitHub repositories implementing machine learning for anemia detection:

1. Anemia Detection with Machine Learning – Uses classification models on blood test data.
2. Anemia Detection Using Random Forest – Web app for anemia diagnosis using Random Forest.
3. Anemia Detection from Conjunctiva Images – Uses CNNs for detecting anemia from eyelid images.
4. Non-Invasive Anemia Detection Using Deep Learning – Smartphone-based anemia detection with deep learning.
5. Predicting Anemia Using Blood Images – CNN-based classification of blood smear images.

Conclusion

Machine learning is revolutionizing anemia detection, offering accurate, fast, and non-invasive diagnostic tools. Whether using classification models or deep learning techniques, AI-driven solutions improve early detection and enhance healthcare accessibility.

🚀 Call to Action

🔹 Try implementing your own anemia detection model using the sample code above!
🔹 Contribute to open-source projects and improve existing anemia detection AI models.
🔹 Share this article with AI and healthcare enthusiasts to promote tech-driven medical solutions!

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