June 2025 in “Jurnal Bumigora Information Technology (BITe)” In this study, researchers aimed to develop a Naive Bayes algorithm to predict hair loss risk based on personal and clinical data, including age, gender, stress levels, hormones, and family history. Results were not reported.
February 2023 in “International Journal of Multimedia Computing” In this study, improved hidden Markov algorithms based on Bayesian methods enhanced the resolution and segmentation accuracy of low-dose CT images significantly more than naive Bayesian methods.
January 2021 in “Lecture notes in networks and systems” In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.
20 citations
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September 2020 in “International journal of computer applications” This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.
19 citations
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October 2024 in “BMC Medical Informatics and Decision Making” This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.
