Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Peksi, Nandha Juniaroesita; Yuwono, Bambang Darmo; Florestiyanto, Mangaras Yanu

doi:10.31315/telematika.v18i1.4587

Cited by 10 publications

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“…Many studies have been conducted with the use of non-invasive techniques such as machine learning algorithms in the detection of anemia mostly with the use of the conjunctiva of the eyes, though the palpable palm is quite less used in most studies as compared to the conjunctiva. With the use of images of the conjunctiva of the eyes and the palpable palm, the authors [ 13 ] classified anemia using the Naïve Bayes which resulted in an accuracy of 90%, while Chand et al . [ 14 ] affirmed that Palmer had an accuracy higher than that of the conjunctiva when assessed the efficiency of the palm, fingernails and the conjunctiva in anemia detection in children aged from two months to five years.…”

Section: Introductionmentioning

confidence: 99%

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

et al. 2023

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Background Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. Methods The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively. Results The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia. Conclusions The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia.

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Section: Introductionmentioning

confidence: 99%

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

et al. 2023

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“…With the use of probability based on a given set of features, the Naïve Bayes classifier allows one to predict a class or category and is termed a probabilistic classifier because it incorporates strong independence assumptions based on models of probability 22 . Because there are no hyperparameters to adjust, the Nave Bayes typically generalizes effectively 7 …”

Section: Methodology and Experimental Designmentioning

confidence: 99%

“…However, there are limited attributes used for only a few blood parameters. 21 The study conducted by Peksi et al 22 used a non-invasive technique to detect anemia with the help of clinical symptoms based on fingernails and palm images with their intensity color. They extracted the RGB component of 20 images and utilized the Naïve Bayes algorithm which achieved an accuracy of 90%.…”

Section: Related Workmentioning

confidence: 99%

“…Gaps in the related works above include the size of the dataset used for the studies. Most studies such as References 8,22,38 used a small‐size of dataset without the use of image augmentation which could affect the performance of the study. This is because machine learning models perform better when trained, validated and tested on huge dataset samples 7,37 .…”

Section: Related Workmentioning

confidence: 99%

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Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

Asare

Appiahene

Donkoh

et al. 2023

Engineering Reports

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Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below the age of 6 and 40% of pregnant women worldwide are anemic. This affects the world's total population by 33%, due to the cause of iron deficiency. The non‐invasive technique, such as the use of machine learning algorithms is one of the methods used in the diagnosis or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, a machine learning approach was used to detect iron‐deficiency anemia with the application of Naïve Bayes, CNN, SVM, k‐NN, and decision tree algorithms. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the color of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The method utilized was categorized into three different stages: dataset collection, dataset preprocessing, and model development for anemia detection. The CNN achieved a higher accuracy of 99.12%, while the SVM had the least accuracy of 95.4%. The performance of the models justifies that the non‐invasive approach is an effective mechanism for anemia detection.

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“…However, there are limited attributes used for only a few blood parameters [18]. The study conducted by [19] used a non-invasive technique to detect anemia with the help of clinical symptoms based on the fingernails and palm with their intensity colour. They extracted the RGB component of 20 images and utilized the Naïve Bayes algorithm which achieved an accuracy of 90%.…”

Section: Related Workmentioning

confidence: 99%

Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images.

Asare

Appiahene

Donkoh

et al. 2023

Preprint

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Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.

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Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Cited by 10 publications

References 0 publications

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images.

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