{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:54:19Z","timestamp":1778255659230,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T00:00:00Z","timestamp":1750464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The New Frontier Research Fund","award":["NFRFE-2019-01365"],"award-info":[{"award-number":["NFRFE-2019-01365"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females\u2019 susceptibility to these diseases based on biological, environmental, and socioeconomic factors. An explainable AI (XAI) technique, local interpretable model-agnostic explanations (LIME), was applied to enhance the transparency and interpretability of the predictive models. This approach provided insights into the models\u2019 decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. Factors such as high cholesterol levels, poor personal hygiene, and exposure to air pollution emerged as significant contributors to disease susceptibility, revealing critical areas for public health intervention in remote and resource-constrained settings. This study demonstrates the effectiveness of integrating XAI with ML in directing health interventions, providing a clearer understanding of risk factors, and efficiently allocating resources for disease prevention and treatment.<\/jats:p>","DOI":"10.3390\/info16070520","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T07:42:34Z","timestamp":1750664554000},"page":"520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2199-5049","authenticated-orcid":false,"given":"Kingsley Friday","family":"Attai","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene 530101, Nigeria"},{"name":"Novena Computers and Technologies Limited, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Constance","family":"Amannah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ignatius Ajuru University of Education, Port Harcourt 500102, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-5259","authenticated-orcid":false,"given":"Moses","family":"Ekpenyong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"},{"name":"STEM Centre, & Centre for Research, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"Baadel","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computing, Mount Royal University, Calgary, AB T3E 6K6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Okure","family":"Obot","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Asuquo","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekerette","family":"Attai","sequence":"additional","affiliation":[{"name":"Novena Computers and Technologies Limited, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faith-Valentine","family":"Uzoka","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science, Engineering and Technology, Texas Southern University, 3100 Cleburne St, Houston, TX 77004, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emem","family":"Dan","sequence":"additional","affiliation":[{"name":"Institute of Health Research and Development, University of Uyo Teaching Hospital, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christie","family":"Akwaowo","sequence":"additional","affiliation":[{"name":"Institute of Health Research and Development, University of Uyo Teaching Hospital, Uyo 520103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faith-Michael","family":"Uzoka","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computing, Mount Royal University, Calgary, AB T3E 6K6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"key":"ref_1","first-page":"20","article-title":"A Scoping Review of the Biological, Socioeconomic and Environmental Determinants of Overweight and Obesity Among Middle Eastern and Northern African Nationalities. Sultan Qaboos Univ","volume":"24","author":"Valdez","year":"2024","journal-title":"Med. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4440","DOI":"10.1289\/isee.2014.ETH-08","article-title":"Outside the Box\u2013An Expanded View of Environmental Factors","volume":"26","author":"Lindert","year":"2014","journal-title":"ISEE Conf. Abstr."},{"key":"ref_3","unstructured":"Winters-Miner, L.A., Bolding, P.S., Hilbe, J.M., Goldstein, M., Hill, T., Nisbet, R., and Miner, G.D. (2015). Practical Predictive Analytics and Decisioning Systems for Medicine, Elsevier."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2174\/1874279301206010025","article-title":"Risk of Infections in Patients with Chronic Diseases","volume":"6","author":"Mor","year":"2012","journal-title":"Open Infect. Dis. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.tree.2009.06.015","article-title":"Disease Spread, Susceptibility and Infection Intensity: Vicious Circles?","volume":"25","author":"Beldomenico","year":"2010","journal-title":"Trends Ecol. Evol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.4103\/1596-3519.55704","article-title":"Tropical Parasitic Diseases and Women","volume":"6","author":"Okwa","year":"2007","journal-title":"Ann. Afr. Med."},{"key":"ref_7","first-page":"1030","article-title":"Urinary Tract Infection in Pregnancy: Review of Clinical Management","volume":"3","author":"Michelim","year":"2016","journal-title":"J. Clin. Nephrol. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"752852","DOI":"10.1155\/2013\/752852","article-title":"Pregnancy and Susceptibility to Infectious Diseases","volume":"2013","author":"Sappenfield","year":"2013","journal-title":"Infect. Dis. Obstet. Gynecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.4103\/CIDS.CIDS_14_24","article-title":"Infections in Pregnancy","volume":"2","author":"Singhal","year":"2024","journal-title":"J. Clin. Infect. Dis. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jain, J.J. (2022). Changing Epidemiology of Infections in Pregnancy: A Global Perspective. Infections and Pregnancy, Springer.","DOI":"10.1007\/978-981-16-7865-3_1"},{"key":"ref_11","first-page":"50","article-title":"Malaria During Pregnancy: Effects on Maternal Morbidity and Mortality","volume":"2","author":"Obeagu","year":"2024","journal-title":"Elite J. Nurs. Health Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.5114\/pm.2021.105382","article-title":"Urinary Tract Infection in Women","volume":"20","author":"Czajkowski","year":"2021","journal-title":"Menopause Rev. Przegl\u0105d Menopauzalny"},{"key":"ref_13","first-page":"875","article-title":"Urinary Tract Infections in Women: Treatment Options and Antibiotic Resistance","volume":"12","author":"Faraz","year":"2020","journal-title":"J. Pharm. Sci. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.ogrm.2017.06.006","article-title":"Urinary Tract Infection in Obstetrics and Gynaecology","volume":"27","author":"Curtiss","year":"2017","journal-title":"Obstet. Ginecol. Reprod. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.idc.2013.09.003","article-title":"Urinary Tract Infection Syndromes: Occurrence, Recurrence, Bacteriology, Risk Factors, and Disease Burden","volume":"28","author":"Foxman","year":"2014","journal-title":"Infect. Dis. Clin. N. Am."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tokatli, M.R., Sisti, L.G., Marziali, E., Nachira, L., Rossi, M.F., Amantea, C., and Malorni, W. (2022). Hormones and Sex-Specific Medicine in Human Physiopathology. Biomolecules, 12.","DOI":"10.3390\/biom12030413"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100759","DOI":"10.1016\/j.tranon.2020.100759","article-title":"Immune Suppression in Pregnancy and Cancer: Parallels and Insights","volume":"13","author":"Kareva","year":"2020","journal-title":"Transl. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mehta, S., and Mann, A. (2022). Pregnancy Changes Predisposing to Infections. Infections and Pregnancy, Springer.","DOI":"10.1007\/978-981-16-7865-3"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1093\/infdis\/jiz670","article-title":"Epidemiology and Clinical Outcomes of Hospitalizations for Acute Respiratory or Febrile Illness and Laboratory-Confirmed Influenza among Pregnant Women during Six Influenza Seasons, 2010\u20132016","volume":"221","author":"Dawood","year":"2020","journal-title":"J. Infect. Dis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dirican, A.\u00d6., Ceran, M.U., \u00d6z\u00e7imen, E.E., \u00c7ulha, A.A., Abas\u0131yan\u0131k, M.A., \u00dcst\u00fcn, B., and Akg\u00fcn, S. (2023). COVID-19 Infection and Women\u2019s Health; Which Women Are More Vulnerable in Terms of Gynecological Health?. Preprint.","DOI":"10.21203\/rs.3.rs-3079652\/v1"},{"key":"ref_21","first-page":"103","article-title":"Gender Disparities in People Living with Obesity-An Unchartered Territory","volume":"12","author":"Kapoor","year":"2021","journal-title":"J. Midlife Health"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Maccioni, L., Weber, S., and Elgizouli, M. (2018). Obesity and Risk of Respiratory Tract Infections: Results of an Infection-Diary-Based Cohort Study. BMC Public Health, 18.","DOI":"10.1186\/s12889-018-5172-8"},{"key":"ref_23","unstructured":"Asuquo, E.F., and Akpan-Idiok, P.A. (2020). The Exceptional Role of Women as Primary Caregivers for People Living with HIV\/AIDS in Nigeria, West Africa. Suggestions for Addressing Clinical and Non-Clinical Issues in Palliative Care, Caregiving and Home Care, IntechOpen."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e067678","DOI":"10.1136\/bmjopen-2022-067678","article-title":"Health Risk Assessment of Indoor Air Quality, Sociodemographic and Kitchen Characteristics on Respiratory Health Among Women Responsible for Cooking in Urban Settings of Oromia Region, Ethiopia: A Community-Based Cross-Sectional Study","volume":"13","author":"Zewdie","year":"2023","journal-title":"BMJ Open"},{"key":"ref_25","first-page":"1","article-title":"Women and Places; Female Street Vendors, Territorial Identity and Placemaking","volume":"2","author":"Saad","year":"2022","journal-title":"J. Art Des."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pradhan, S., Raksha, G.S., and Akhil, P. (2024). Role of Women in Food and Agricultural Development: Breaking Barriers for Sustainable Growth. Impact of Women in Food and Agricultural Development, IGI Global.","DOI":"10.4018\/979-8-3693-3037-1.ch008"},{"key":"ref_27","unstructured":"Statista (2024, September 21). Global Adult Literacy Rate from 2000 to 2022, by Gender. Available online: https:\/\/www.statista.com\/statistics\/1220131\/global-adult-literacy-rate-by-gender\/."},{"key":"ref_28","unstructured":"Jimoh, R., Adamu, A., Oyewobi, L., and Bajere, P. (2024, September 21). How Women Are Locked Out of Nigeria\u2019s Construction Industry. The Conversation. Available online: https:\/\/theconversation.com\/how-women-are-locked-out-of-nigerias-construction-industry-157643#:~:text=In%20Nigeria%2C%20women%20make%20up,ethics%20and%20values%20in%20Nigeria."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101946","DOI":"10.1016\/j.tmaid.2020.101946","article-title":"Typhoid Fever Infection\u2013Antibiotic Resistance and Vaccination Strategies: A Narrative Review","volume":"40","author":"Atouguia","year":"2021","journal-title":"Travel Med. Infect. Dis."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Muhammad, B., and Varol, A. (2021, January 28\u201329). A Symptom-Based Machine Learning Model for Malaria Diagnosis in Nigeria. Proceedings of the 2021 9th International Symposium on Digital Forensics and Security (ISDFS), Elazig, Turkey.","DOI":"10.1109\/ISDFS52919.2021.9486315"},{"key":"ref_31","first-page":"89","article-title":"Web-Based Diagnosis of Typhoid and Malaria Using Machine Learning","volume":"1","author":"Odion","year":"2024","journal-title":"Nigerian Defence Academy J. Military Sci. Interdiscip. Stud."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Attai, K., Ekpenyong, M., Amannah, C., Asuquo, D., Ajuga, P., Obot, O., Johnson, E., John, A., Maduka, O., and Akwaowo, C. (2024). Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models. Trop. Med. Infect. Dis., 9.","DOI":"10.20944\/preprints202408.0966.v1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1515\/cclm-2022-1006","article-title":"Developing a Machine Learning Prediction Algorithm for Early Differentiation of Urosepsis from Urinary Tract Infection","volume":"61","author":"Su","year":"2023","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_34","first-page":"2199","article-title":"Analysis, Prediction and Evaluation of COVID-19 Datasets Using Machine Learning Algorithms","volume":"8","author":"Prakash","year":"2020","journal-title":"Int. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kumarakulasinghe, N.B., Blomberg, T., Liu, J., Leao, A.S., and Papapetrou, P. (2020, January 28\u201330). Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models. Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA.","DOI":"10.1109\/CBMS49503.2020.00009"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Attai, K., Akwaowo, C., Asuquo, D., Esubok, N.E., Nelson, U.A., Dan, E., and Uzoka, F.M. (2023). Explainable AI Modelling of Comorbidity in Pregnant Women and Children with Tropical Febrile Conditions. Proc. Int. Conf. Artif. Intell. Appl., 152\u2013159.","DOI":"10.59200\/ICARTI.2023.022"},{"key":"ref_37","unstructured":"University of Uyo Teaching Hospital, and Mount Royal University (2024). NFRF Project Patient Dataset with Febrile Diseases [Data Set], Zenodo."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s13370-024-01198-1","article-title":"Random Forest Classifier for High Entropy Alloys Phase Diagnosis","volume":"35","author":"Yousefi","year":"2024","journal-title":"Afr. Mat."},{"key":"ref_39","first-page":"317","article-title":"Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach","volume":"Volume 218","author":"Bianchini","year":"2022","journal-title":"Advanced Computing and Intelligent Technologies"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1080\/10494820.2021.1928235","article-title":"Enhancing the Prediction of Student Performance Based on the Machine Learning XGBoost Algorithm","volume":"31","author":"Asselman","year":"2021","journal-title":"Interact. Learn. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhang, L., Bhatti, U.A., and Huang, M. (2023). Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach. Diagnostics, 13.","DOI":"10.3390\/diagnostics13162681"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:56:24Z","timestamp":1760032584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,21]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070520"],"URL":"https:\/\/doi.org\/10.3390\/info16070520","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,21]]}}}