LightGBM empowered by whale optimization for thyroid disease detection

Sinha, Bam Bahadur; Ahsan, Mohammad; Dhanalakshmi, R.

doi:10.1007/s41870-023-01261-3

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…A crucial role in Thyroid illness detection is played by a framework design proposed by Sinha et al 18 that uses LightGBM, Sequential Backward Selection (SBS), and a metaheuristic approach called Whale Optimisation (WO). The primary purpose of this study is to deliver a method that is both extremely precise and logical for identifying human thyroid problem.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Jopate,

Pareek,

et al. 2024

Baghdad Sci.J

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كثيرًا ما يعاني الأشخاص الذين يعانون من قصور الغدة الدرقية من أعراض حادة. يؤدي التصنيف الصحيح والتعلم الآلي إلى تحسين تشخيص أمراض الغدة الدرقية بشكل كبير. سيؤثر هذا التصنيف الدقيق على تقديم الرعاية للمرضى في الوقت المناسب. على الرغم من وجود تقنيات التشخيص، فإنها تسعى في كثير من الأحيان إلى التصنيف الثنائي، وتستخدم مجموعات بيانات كبيرة غير كافية، وتفتقر إلى تأكيد استنتاجاتها. تركز الأساليب الحالية على تحسين النموذج، في حين يتم إهمال هندسة الميزات. يقدم هذا البحث نموذج خوارزمية تحسين قطيع الفيل التكيفي AEHOA لاختيار السمات المثالية من أجل التحايل على هذه القيود. في البداية، استخدم طريقة تسمى تقنية الإفراط في أخذ العينات للأقلية الاصطناعية SMOTE لتسوية البيانات. وأخيرًا، يتم إدخال معلمات نموذج AEHOA في الشبكة العصبية التلافيفية CNN لتصنيف البيانات وتعزيز التنبؤ. تمت أيضًا زيادة دقة تنبؤات التصنيف عن طريق تعديل مجموعة البيانات. تم إخضاع مجموعتي البيانات لعملية تصنيف لإجراء مقارنة أكثر دقة للنتائج.

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Section: Related Workmentioning

confidence: 99%

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Jopate,

Pareek,

et al. 2024

Baghdad Sci.J

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“…LightGBM is an efficient gradient-boosting decision tree method known for its exceptional computational efficiency and remarkable accuracy [22,32,33]. It effectively tackles complex problems by efficiently handling large datasets, leading to faster training and prediction times.…”

Section: Lightgbmmentioning

confidence: 99%

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

Noviandy,

Maulana,

Idroes

et al. 2023

Malacca Pharm.

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This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.

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Deep Multilayer Neural Network with Weights Optimization-Based Genetic Algorithm for Predicting Hypothyroid Disease

El-Hassani,

Fatih,

Joudar

et al. 2023

Arab J Sci Eng

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LightGBM empowered by whale optimization for thyroid disease detection

Cited by 10 publications

References 25 publications

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

Deep Multilayer Neural Network with Weights Optimization-Based Genetic Algorithm for Predicting Hypothyroid Disease

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