Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma

Chicco, Davide; Oneto, Luca

doi:10.1177/1460458220984205

Cited by 16 publications

(20 citation statements)

References 113 publications

(209 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While in the case of HCC, it is the fourth most regular reason for cancer-related mortality in the world [4]. It happens mainly in chronic liver ailments, such as cirrhosis induced by hepatitis C or hepatitis B [5]. It affected 14 million people worldwide in 2012 [5].…”

Section: Introductionmentioning

confidence: 99%

“…It happens mainly in chronic liver ailments, such as cirrhosis induced by hepatitis C or hepatitis B [5]. It affected 14 million people worldwide in 2012 [5]. However, liver cancer kills approximately eight lakhs of people worldwide annually [5].…”

Section: Introductionmentioning

confidence: 99%

“…It affected 14 million people worldwide in 2012 [5]. However, liver cancer kills approximately eight lakhs of people worldwide annually [5]. If the disease is detected early, it can potentially be treated with surgery or a transplant.…”

Section: Introductionmentioning

confidence: 99%

“…If the disease is detected early, it can potentially be treated with surgery or a transplant. This type of ailment most frequently occurs in men within 30 to 50 years old and is exhibited frequently in Mongolia, China, Sub-Saharan Eastern and Western Africa, and South-East Asia [5]. Like another type of cancer, HCC cells regenerate faster and evade apoptosis [5].…”

Section: Introductionmentioning

confidence: 99%

“…This type of ailment most frequently occurs in men within 30 to 50 years old and is exhibited frequently in Mongolia, China, Sub-Saharan Eastern and Western Africa, and South-East Asia [5]. Like another type of cancer, HCC cells regenerate faster and evade apoptosis [5]. The immediate diagnosis and accurate analysis of such diseases are deserving of healthcare research and may lessen medication expenses and support rescue human life [3].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

View full text Add to dashboard Cite

Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

show abstract

Section: Introductionmentioning

confidence: 99%