article · Medicine in Novel Technology and Devices
Anemia is a widespread global health condition affecting red blood cells and haemoglobin levels, particularly among pregnant women and young children. Standard diagnostic methods are often invasive, costly, and time-consuming, creating challenges in resource-constrained regions. To support non-invasive screening, palpable palm photographs were collected from 710 participants across selected hospitals in Ghana. The collected images underwent extraction, segmentation, and conversion into RGB percentiles to train and test several machine learning models. Using the R programming language, a hybrid architecture was developed using ensemble techniques, including voting, boosting, bagging, and stacking. The stacking ensemble model attained a diagnostic accuracy of 99.73 per cent. These findings show that ensemble machine learning approaches can deliver highly accurate, non-invasive detection of anemia, offering a practical alternative for medical diagnosis where healthcare personnel and laboratory resources are limited.
Anemia poses severe health risks, yet traditional blood tests require invasive sampling, laboratory facilities, and skilled staff that are often scarce in developing communities. A non-invasive method using standard palm photographs offers a rapid, low-cost screening option, enabling earlier intervention and better disease management in settings with limited clinical infrastructure.
This research demonstrates an applied algorithmic approach that could enable non-invasive point-of-care diagnostic tools for medical staff in resource-constrained clinics. Because the method has been trained and validated on hospital patient images rather than integrated into a certified digital diagnostic application or medical device, it remains at an applied research stage that requires software product development and regulatory clearance before clinical adoption.
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Anemia is a public health issue with serious ramifications for human health globally. Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk. Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed. To discover medical remedies on time, early detection or diagnosis of anemia assist patients to understand their condition. The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate. This study uses palpable palm images (dataset) collected from 710 participants in selected hospitals in Ghana. The images were extracted, segmented and converted into RGB percentile to train, validate and tested the machine learning models. A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform. Stacking, voting, boosting and bagging ensemble model techniques were used to build the hybrid models, the stacking ensemble model achieved an accuracy of 99.73 %. The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.
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DOI: 10.1016/j.medntd.2023.100269
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