Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

Mariki, Martina; Mkoba, Elizabeth; Mduma, Neema

doi:10.1080/08839514.2022.2031826

Cited by 16 publications

(10 citation statements)

References 77 publications

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“…This is in line with recent studies which have identified similar risk factors such as fever and headache (Bria et al, 2021;Trampuz et al, 2003).Fever has been found as the second most common symptom among patients with Plasmodium .falciparum infection in Indonesia after the duration of fever (Bria et al, 2021), suggesting it as a useful symptom for identifying patients with malaria, particularly in areas of high transmission. A similar observation was made by Mariki et al, 2022, confirming the usefulness of fever in diagnosing patients with malaria, particularly in areas of high transmission.…”

Section: Discussionsupporting

confidence: 80%

“…Information on the age, sex, body weight, height, body mass index, body temperature, fever, diarrhea, vomiting, headache, cough, sore throat, dizziness, muscle pain, presence of stagnant water at home, presence of stagnant water at the workplace, presence of bushes in surroundings, and usage of mosquito repellant were collected from the patients. This information was collected because the variables have been commonly found to be associated with the risk of malaria (Mariki et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Ojurongbe

Afolabi

Akinde

et al. 2023

Preprint

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Background: The current malaria diagnosis methods, which rely on microscopy and Histidine Rich Protein2 (HRP2) based RDT, have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all the limitations. Consequently, automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. This study, therefore, used a machine learning model to predict the occurrence of malaria based on socio-demographicbehaviour, environment, and clinical features. Methods:Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward features selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Results: Among the three machine learningmodels examined, penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve performance for the training set (84%; 95% confidence interval (CI) =75–93%) and test set (83%; 95% CI =63–100%). The model included age, BMI, body temperature, bushes in surroundings, body weight, dizziness, fever, headache, mosquito repellant, muscle pain, sex, sore throat, stagnant water in the home, and vomiting. An increased odd of patients having malaria was associated with high body weight (adjusted odd ratio (AOR) = 4.50, 95% CI =2.27-8.01, p-value <0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients who had body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI =0.99-1.91, p-value = 0.068). Also, patients who had bushes in the surroundings (AOR = 2.60, 95% CI =1.30-4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI =0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI =0.95–3.95, p-value = 0.068), muscle pain (AOR =1.49; CI =0.66–3.39, p-value = 0.333) and vomiting (AOR = 2.32; CI =0.85–6.82, p-value = 0.097) were more likely to experience malaria compared to those without bushes in the surrounding or those who did not experience fever, headache, muscle pain and vomiting. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI= 0.41-0.90, p-value = 0.012) or BMI (AOR = 0.47, 95% CI= 0.26-0.80, p-value =0.006). Conclusion:Newly developed routinely collected baseline socio-demographical, environmental, and clinical features topredict malaria types and may serve as a valuable tool for clinical decision making.

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Section: Discussionsupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Ojurongbe

Afolabi

Akinde

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Information on age, sex, body weight, height, body mass index, body temperature, fever, diarrhea, vomiting, headache, cough, sore throat, dizziness, muscle pain, presence of stagnant water at home, presence of stagnant water in the workplace, presence of bushes in the surroundings, and usage of mosquito repellants were collected from the patients. This information was collected because these variables are commonly associated with malaria risk (40).…”

Section: Methodsmentioning

confidence: 99%

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Ojurongbe

Afolabi

Bashiru

et al. 2023

Preprint

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Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all of these limitations. Consequently, the automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. Therefore, this study used a machine-learning model to predict the occurrence of malaria based on sociodemographic behaviour, environment, and clinical features.Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models.Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve for the training set (84%; 95% confidence interval (CI) = 75–93%) and test set (83%; 95% CI = 63–100%). Increased odds of malaria was associated with high body weight (adjusted odds ratio (AOR) = 4.50, 95% CI = 2.27–8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients with body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI = 0.99–1.91, p-value = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI = 1.30–4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI = 0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI = 0.95–3.95, p-value = 0.068), muscle pain (AOR = 1.49; CI = 0.66–3.39, p-value = 0.333), and vomiting (AOR = 2.32; CI = 0.85–6.82, p-value = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI = 0.41–0.90, p-value = 0.012) and BMI (AOR = 0.47, 95% CI = 0.26–0.80, p = 0.006).Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict malaria types may serve as a valuable tool for clinical decision-making.

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“…The VGG-19 model is fine-tuned to classify the malaria cells. This method also requires to adoption of the best features selection method to improve the accuracy ( Mariki, Mkoba & Mduma, 2022 ). The random forest classifier is applied to classify the malaria cells.…”

Section: Related Workmentioning

confidence: 99%

Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization

Amin,

Almas Anjum,

Ahmad

et al. 2024

PeerJ Computer Science

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Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work.

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Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

Cited by 16 publications

References 77 publications

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization

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