Classification of anemic condition based on photoplethysmography signals and clinical dataset

One of the worldwide public health issues mostly affecting children and expectant mothers is Anemia. Recently, non-invasive hemoglobin (Hb) measurements, such as machine learning (ML) algorithms, can diagnose Anemia more quickly and efficiently.

To diagnose Anemia using photoplethysmography (PPG), two tracks are investigated in this paper, based on clinical data and PPG signals. We use state-of-the-art data for Hb levels, extracted from PPG signals. This first track's methodology is divided into three stages: the labelling of the data as normal and abnormal; the data pre-processing; and applying ML algorithms based on four given features. We extracted nineteen features for red and infrared measurements in the second track. The second track's methodology is broken down into five stages: labelling of the data; data processing; signal augmentation; feature extraction; and applying ML algorithms. A five-fold cross-validation technique was applied for both tracks.

We succeeded in classifying the anemic condition with 100 % classification accuracy. Our accurate detection of anemic status will promote preventive healthcare.

Ultimately, this proposed ML model in this paper validated the effectiveness of the ML algorithms as non-invasive techniques for identifying Anemia.