Clinical malaria diagnosis: rule-based classification statistical prototype

Francis Bbosa, Ronald Wesonga*, Peter Jehopio

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

8 اقتباسات (Scopus)

ملخص

In this study, we identified predictors of malaria, developed data mining, statistically enhanced rule-based classification to diagnose malaria and developed an automated system to incorporate the rules and statistical models. The aim of the study was to develop a statistical prototype to perform clinical diagnosis of malaria given its adverse effects on the overall healthcare, yet its treatment remains very expensive for the majority of the patients to afford. Model validation was performed using records from two hospitals (training and predictive datasets) to evaluate system sensitivity, specificity and accuracy. The overall sensitivity of the rule-based classification obtained from the predictive dataset was 70 % [68–74; 95 % CI] with a specificity of 58 % [54–66; 95 % CI]. The values for both sensitivity and specificity varied by age, generally showing better performance for the data mining classification rules for the adult patients. In summary, the proposed system of data mining classification rules provides better performance for persons aged at least 18 years. However, with further modelling, this system of classification rules can provide better sensitivity, specificity and accuracy levels. In conclusion, using the system provides a preliminary test before confirmatory diagnosis is conducted in laboratories.

اللغة الأصليةEnglish
رقم المقال939
دوريةSpringerPlus
مستوى الصوت5
رقم الإصدار1
المعرِّفات الرقمية للأشياء
حالة النشرPublished - ديسمبر 1 2016
منشور خارجيًانعم

ASJC Scopus subject areas

  • ???subjectarea.asjc.1000???

بصمة

أدرس بدقة موضوعات البحث “Clinical malaria diagnosis: rule-based classification statistical prototype'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا