Machine Learning and Multivariate Analysis of Hemorheological Dysfunction and Clinical Outcomes in HbSS Patients with Malaria Co-Infection

References

[1] Adigwe OP, Onaybayba G, Onoja SO (2023). Impact of sickle cell disease on affected individuals in Nigeria: A critical review. International Journal of General Medicine, 16, 2307–2320. https://doi.org/10.2147/IJGM.S410015

[2] Aidoo M, Terlouw DJ, Kolczak MS, McElroy PD, ter Kuile FO, Kariuki S, et al. (2002). Protective effects of the sickle cell gene against malaria morbidity and mortality. The Lancet, 359(9314), 1311–1312. https://doi.org/10.1016/S0140-6736(02)08273-9

[3] Bunn HF (2013). The triumph of good over evil: Protection by the sickle gene against malaria. Blood, 121(1), 20–25. https://doi.org/10.1182/blood-2012-08-449397

[4] Chen CX, Funkenbusch GT, Wax A (2023). Biophysical profiling of sickle cell disease using holographic cytometry and deep learning. International Journal of Molecular Sciences, 24(15), 11885. https://doi.org/10.3390/ijms241511885

[5] de Haan K, Koydemir HC, Rivenson Y, Tseng D, Van Dyne E, Bakic L, et al. (2020). Automated screening of sickle cells using a smartphone-based microscope and deep learning. npj Digital Medicine, 3, Article 76. https://doi.org/10.1038/s41746-020-0282-y

[6] Ekong B, Ekong O, Silas A, Edet AE, William B (2023). Machine learning approach for classification of sickle cell anaemia in teenagers based on Bayesian network. Journal of Information Systems and Informatics, 5(4), 1607–1621. https://doi.org/10.51519/journalisi.v5i4.629

[7] Elsabagh AA, Elhadary M, Elsayed B, Elshoebi AM, Ferih K, Kaddoura R, et al. (2023). Artificial intelligence in sickle cell disease. Blood Reviews, 61, 101102. https://doi.org/10.1016/j.blre.2023.101102

[8] Goswami NG, Goswami A, Sampathila N, Muralidhar GB, Chadaga K, Belurkar S (2024). Detection of sickle cell disease using deep neural networks and explainable artificial intelligence. Journal of Intelligent Systems, 33(1), Article 20230179. https://doi.org/10.1515/jisys-2023-0179

[9] Komba AN, Makani J, Sadarangani M, Ajala-Agbo T, Berkley JA, Newton CRJC, et al. (2009). Malaria as a cause of morbidity and mortality in children with homozygous sickle cell disease on the coast of Kenya. Clinical Infectious Diseases, 49(2), 216–222. https://doi.org/10.1086/599037

[10] Lopera-Mesa TM, Doumbia S, Konaté D, Anderson JM, Doumbouya M, Keita AS, et al. (2015). Effect of red blood cell variants on childhood malaria in Mali: A prospective cohort study. The Lancet Haematology, 2(4), e140–e149. https://doi.org/10.1016/S2352-3026(15)00043-5

[11] Luzzatto L (2012). Sickle cell anaemia and malaria. Mediterranean Journal of Hematology and Infectious Diseases, 4(1), e2012065. https://doi.org/10.4084/MJHID.2012.065

[12] Machado TF, Barros Neto FC, Gonçalves MS, Barbosa CG, Barreto ME (2024). Exploring machine learning algorithms in sickle cell disease patient data: A systematic review. PLoS ONE, 19(11), e0313315. https://doi.org/10.1371/journal.pone.0313315

[13] Makani J, Komba AN, Cox SE, Oruo J, Mwamtemi K, Kitundu J, et al. (2010). Malaria in patients with sickle cell anemia: Burden, risk factors, and outcome at the outpatient clinic and during hospitalization. Blood, 115(2), 215–220. https://doi.org/10.1182/blood-2009-07-233528

[14] May J, Evans JA, Timmann C, Ehmen C, Busch W, Thye T, et al. (2007). Hemoglobin variants and disease manifestations in severe falciparum malaria. JAMA, 297(20), 2220–2226. https://doi.org/10.1001/jama.297.20.2220

[15] McAuley CF, Webb C, Makani J, Macharia A, Uyoga S, Opi DH, et al. (2010). High mortality from Plasmodium falciparum malaria in children living with sickle cell anemia on the coast of Kenya. Blood, 116(10), 1663–1668. https://doi.org/10.1182/blood-2010-01-265249

[16] Petrović N, Moyà-Alcover G, Jaume-i-Capó A, González-Hidalgo M (2020). Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images. Computers in Biology and Medicine, 126, 104027. https://doi.org/10.1016/j.compbiomed.2020.104027

[17] Piel FB, Patil AP, Howes RE, Nyangiri OA, Gething PW, Williams TN, et al. (2010). Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis. Nature Communications, 1, 104. https://doi.org/10.1038/ncomms1104

[18] Piel FB, Hay SI, Gupta S, Weatherall DJ, Williams TN (2013). Global burden of sickle cell anaemia in children under five, 2010-2050: Modelling based on demographics, excess mortality, and interventions. PLoS Medicine, 10(7), e1001484. https://doi.org/10.1371/journal.pmed.1001484

[19] Piel FB, Steinberg MH, Rees DC (2017). Sickle cell disease. The New England Journal of Medicine, 376(16), 1561–1573. https://doi.org/10.1056/NEJMra1510865

[20] Quinn CT (2016). Clinical severity in sickle cell disease: The challenges of definition and prognostication. Experimental Biology and Medicine, 241(7), 679–688. https://doi.org/10.1177/1535370216640385

[21] Roy SK, Gupta S, Jain P (2024). Machine learning-based disease severity prediction in sickle cell patients: Spectroscopic insights. In: Proceedings of the 17th International Conference on Health Informatics (HEALTHINF 2024). SCITEPRESS. p. 665–672. https://doi.org/10.5220/0012343800003657

[22] Sadafi A, Bordukova M, Makhro A, Navab N, Bogdanova A, Marr C (2023). RedTell: An AI tool for interpretable analysis of red blood cell morphology. Frontiers in Physiology, 14, Article 1058720. https://doi.org/10.3389/fphys.2023.1058720

[23] Taylor SM, Parobek CM, Fairhurst RM (2012). Haemoglobinopathies and the clinical epidemiology of malaria: A systematic review and meta-analysis. The Lancet Infectious Diseases, 12(6), 457–468. https://doi.org/10.1016/S1473-3099(12)70055-5

[24] Ussher FA, Okyere AA, Appiah MA (2025). Identification of hematological biomarkers and assessment of machine learning models for sickle cell anemia severity classification. Journal of Sickle Cell Disease, 2(1), yoaf020. https://doi.org/10.1093/jscd/yoaf020

[25] Ware RE, de Montalembert M, Tshilolo L, Abboud MR (2017). Sickle cell disease. The Lancet, 390(10091), 311–323. https://doi.org/10.1016/S0140-6736(17)30193-9

[26] Williams TN, Mwangi TW, Wambua S, Peto TEA, Weatherall DJ, Gupta S, et al. (2005). Sickle cell trait and the risk of Plasmodium falciparum malaria and other childhood diseases. The Journal of Infectious Diseases, 192(1), 178–186. https://doi.org/10.1086/430744

@article{1724585,
      author = {Ajayi J. B., Agbeyangi, O. A., Olalekan R. A., Ige, I. P.},
      title = {Machine Learning and Multivariate Analysis of Hemorheological Dysfunction and Clinical Outcomes in HbSS Patients with Malaria Co-Infection},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {1699-1722},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724585.pdf},
      abstract = {Background: Malaria co-infection may aggravate hemorheological abnormalities and clinical morbidity among individuals with homozygous sickle cell disease (HbSS). However, the relationships between malaria status, blood rheology, hematological characteristics, and clinical outcomes remain incompletely characterized. This study assessed these relationships and evaluated the predictive utility of statistical and machine-learning approaches.

Methods: A retrospective cross-sectional analytical study included 45 laboratory-confirmed HbSS patients, comprising 37 malaria-positive and 8 malaria-negative individuals. Demographic, parasitological, hematological, hemorheological, and clinical variables were analysed using Welch’s t-test, correlation analysis, multivariable regression, K-means clustering, principal component analysis (PCA), and machine-learning models. The analytical workflow was implemented in Google Colaboratory using Python.

Results: Malaria-positive patients had higher blood viscosity (4.80 ± 0.34 and 3.80 ± 0.43 mPa; p < 0.001) and lower erythrocyte deformability (0.88 ± 0.03 and 0.92 ± 0.02 mPa; p < 0.001) than malaria-negative patients. They also had lower haemoglobin and haematocrit, higher reticulocyte and white blood cell counts, more frequent pain crises, and longer hospitalization. Multiple regression identified pain-crisis frequency and blood viscosity as significant predictors of hospitalization duration. K-means clustering identified three patient groups with progressively higher viscosity and lower deformability. PCA showed that two components explained 72.4% of total variance. Blood viscosity discriminated malaria status with an AUC of 0.94 (95% CI: 0.86-0.99); at 4.25 mPa·s, sensitivity and specificity were 91.9% and 87.5%, respectively. Random Forest regression achieved R2 = 0.72 for hospitalization duration.

Conclusion: Malaria co-infection was associated with greater hemorheological impairment and clinical burden among HbSS patients. These findings support further evaluation of hemorheological biomarkers and validated predictive models in larger longitudinal cohorts.},
      keywords = {HbSS; sickle cell disease; malaria co-infection; hemorheology; blood viscosity; erythrocyte deformability; machine learning; hospitalization},
      month = {November},
      doi = {https://doi.org/10.64388/ire1724585}
  }