Prediction of Liver Disease using Gradient Boost Machine Learning Techniques with Feature Scaling

Shobana, G.; Umamaheswari, K.

doi:10.1109/iccmc51019.2021.9418333

Cited by 19 publications

(5 citation statements)

References 13 publications

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“…Contemporary machine learning techniques are used in several healthcare applications [ 28 , 36 , 37 , 38 ]. They are employed to predict future diseases and offer a desirable decision from a data set.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have used machine learning algorithms to indicate diseases such as liver disease (logistic regression with 95.8% accuracy) [ 38 ], breast cancer (support vector machine with 99% accuracy) [ 39 ], and Alzheimer’s disease (neural networks with 98.3% accuracy) [ 40 ]. There are previous studies that have also proposed various machine learning algorithms to predict and classify ADHD.…”

Section: Related Workmentioning

confidence: 99%

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Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Chatpreecha

Usanavasin

2023

Children

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Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Chatpreecha

Usanavasin

2023

Children

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“…A potent ensemble machine learning approach called gradient boosting is well renowned for its prowess in addressing classification and regression issues [9]. It is part of a larger class of augmentation methods that focus on minimizing the error of previous models by assigning higher weights to misclassified instances, iteratively learning and improving them to build strong predictive models.…”

Section: Gradient Boostingmentioning

confidence: 99%

Research on Prediction of Liver Disease Based on Machine Learning Models

2023

HSET

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Liver disease is a disease that has attracted much attention in the world. Liver diseases such as cirrhosis and liver cancer are the common causes of death in the world. Many liver diseases have no obvious symptoms in the early stage of onset, so they are easily overlooked by people. Treatment for liver illnesses is crucial and relies heavily on early diagnosis and management. This study assessed the effectiveness of various machine learning approaches for the identification of liver disease due to the high cost and complexity of the diagnostic process. This research used five machine learning models to predict the presence of liver disease based on a patient's medical records using an Indian liver patient record dataset. The dataset was prepared for model training through data preprocessing and analysis, including handling missing values, encoding categorical variables, and normalizing features. Five machine learning algorithms were evaluated, with Random Forest emerging as the highest performing model with an accuracy of 73.56% on the test set. This study contributes to the field by demonstrating the potential of machine learning to accurately predict liver disease, aiding in early diagnosis and treatment.

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“…For classification, the Light Gradient Boosting Machine, which is built on a Decision Tree algorithm. It grows the tree leaf by leaf rather than level by level [11].…”

Section: Light Gradient Boosting Machine (Lgbm)mentioning

confidence: 99%

Skin Type Classifier

Parshionikar¹

2022

IJSREM

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Variations in skin diseases largely fall into the subset of skin types. Classification of Skin Types plays an essential part in the classification of skin diseases. The paper proposes an end to-end machine learning model used to classify skin type as dry, oily, or normal using Python Libraries. Skin type classification is done using Gray-Level Co-Occurrence Matrix (GLCM) for texture identification and Light Gradient Boosting Machine (LGBM). Red Green Blue (RGB) images are first resized and then converted into Gray-Scale images and then features like energy, correlation, dissimilarity, homogeneity, contrast, and entropy are extracted from a given image using GLCM. Such features give us a basic understanding of the type and texture of the image. The GLCM of the image is calculated by changing the distance and angle of the pixels within the image in a regular interval. These features after extraction are fed to the LGBM algorithm. The trained model is then deployed using Flask. Key Words: features extraction, GLCM, LGBM, skin type

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Prediction of Liver Disease using Gradient Boost Machine Learning Techniques with Feature Scaling

Cited by 19 publications

References 13 publications

Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Research on Prediction of Liver Disease Based on Machine Learning Models

Skin Type Classifier

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