Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.展开更多
Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lowe...Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.展开更多
Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection ...Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.展开更多
One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challengi...One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.展开更多
文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
文摘Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.
基金supported by Research Fund from University of Johannes-burg,Johannesburg City,South Africa.
文摘Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.
文摘One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.
