scholarly journals Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

10.2196/17580 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17580 ◽  
Author(s):  
Seyed Ahmad Hosseini ◽  
Amir Jamshidnezhad ◽  
Marzie Zilaee ◽  
Behzad Fouladi Dehaghi ◽  
Abbas Mohammadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Allergic Asthma ◽  
Crocus Sativus ◽  
Prediction System ◽  
Clinical Prediction ◽  
Testing Data ◽  
Hs Crp ◽  
Supplement Therapy ◽  
Crocus Sativus L

Background Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

scholarly journals Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study (Preprint)

2019 ◽  
Author(s):  
Seyed Ahmad Hosseini ◽  
Amir Jamshidnezhad ◽  
Marzie Zilaee ◽  
Behzad Fouladi Dehaghi ◽  
Abbas Mohammadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Allergic Asthma ◽  
Crocus Sativus ◽  
Prediction System ◽  
Clinical Prediction ◽  
Testing Data ◽  
Hs Crp ◽  
Supplement Therapy ◽  
Crocus Sativus L

BACKGROUND Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. <i>Crocus sativus L</i>, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. OBJECTIVE The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of <i>C sativus L</i> supplements on patients with allergic asthma. METHODS A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of <i>C sativus L</i> using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of <i>C sativus L</i> supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using <i>C sativus L</i> supplement therapy. RESULTS The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV<sub>1</sub>), forced vital capacity (FVC), the ratio of FEV<sub>1</sub>/FVC, and forced expiratory flow (FEF<sub>25%-75%</sub>) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV<sub>1</sub>: 98.1%; FVC: 97.5%; FEV<sub>1</sub>/FVC ratio: 97%; and FEF<sub>25%-75%</sub>: 96.7%, respectively). CONCLUSIONS The clinical prediction system developed in this study was effective in predicting the effect of <i>C sativus L</i> supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

Automobile Engine Fault Diagnosis and Prediction System

2014 ◽  
Vol 1008-1009 ◽  
pp. 641-644
Author(s):  
Mei Lan Zhou ◽  
Ji Chang Wang ◽  
Yan Ping Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fault Prediction ◽  
Prediction System ◽  
Framework Structure ◽  
Hardware Platform ◽  
Automobile Engine ◽  
Engine Vibration ◽  
Abnormal Sound

Aimed at the fault diagnosis and prediction of automobile engine, firstly designed a framework structure of automobile engine fault diagnosis and prediction system, and built a hardware platform; Secondly adopted the genetic algorithm neural network to fault prediction and diagnosis reasoning; Finally after analyzing automobile exhaust components, engine vibration, engine abnormal sound parameters, inferred the appeared and impending fault of automobile then made the tips for users on the screen. The results show that the performance of system is well, the accuracy of diagnosis and prediction is 95% in different conditions of experiment and debugging.

scholarly journals Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan

2019 ◽  
Vol 5 (2) ◽  
pp. 128
Author(s):  
Mustaqim Mustaqim ◽  
Budi Warsito ◽  
Bayu Surarso
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Good Prediction ◽  
Prediction System ◽  
Success Rates ◽  
Backpropagation Neural Network ◽  
Testing Data ◽  
Contraceptive Implant ◽  
Contraceptive Implants ◽  
Major Data

Combination of Synthetic Minority Oversampling Technique (SMOTE) and Backpropagation Neural Network to handle imbalanced class in predicting the use of contraceptive implants  Kegagalan akibat pemakaian alat kontrasepsi implan merupakan terjadinya kehamilan pada wanita saat menggunakan alat kontrasepsi secara benar. Kegagalan pemakaian kontrasepsi implan tahun 2018 secara nasional sejumlah 1.852 pengguna atau 4% dari 41.947 pengguna. Rasio angka kegagalan dan keberhasilan pemakaian kontrasepsi implan yang cenderung tidak seimbang (imbalance class) membuatnya sulit diprediksi. Ketidakseimbangan data terjadi jika jumlah data suatu kelas lebih banyak dari data lain. Kelas mayor merupakan jumlah data yang lebih banyak, sedangkan kelas minor jumlahnya lebih sedikit. Algoritma klasifikasi akan mengalami penurunan performa jika menghadapi kelas yang tidak seimbang. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan data kegagalan pemakaian kontrasepsi implan. SMOTE menghasilkan akurasi yang baik dan efektif daripada metode oversampling lainnya dalam menangani imbalance class karena mengurangi overfitting. Data yang sudah seimbang kemudian diprediksi dengan Neural Network Backpropagation. Sistem prediksi ini digunakan untuk mendeteksi apakah seorang wanita mengalami kehamilan atau tidak jika menggunakan kontrasepsi implan. Penelitian ini menggunakan 300 data, terdiri dari 285 data mayor (tidak hamil) dan 15 data minor (hamil). Dari 300 data dibagi menjadi dua bagian, 270 data latih dan 30 data uji. Dari 270 data latih, terdapat 13 data latih minor dan 257 data latih mayor. Data latih minor pada data latih diduplikasi sebanyak data pada kelas mayor sehingga jumlah data latih menjadi 514, terdiri dari 257 data mayor, 13 data minor asli, dan 244 data minor buatan. Sistem prediksi menghasilkan nilai akurasi sebesar 96,1% pada epoch ke-500 dan 1.000. Implementasi kombinasi SMOTE dan Neural Network Backpropagation terbukti mampu memprediksi pada imbalance class dengan hasil prediksi yang baik.  The failed contraceptive implant is one of the sources of unintended pregnancy in women. The number of users experiencing contraceptive-implant failure in 2018 was 1,852 nationally or 4% out of 41,947 users. The ratio between failure and success rates of contraceptive implant, which tended to be unbalanced (imbalance class), made it difficult to predict. Imbalance class will occur if the amount of data in one class is bigger than that in other classes. Major classes represent a bigger amount of data, while minor classes are smaller ones. The imbalance class will decrease the performance of the classification algorithm. The Synthetic Minority Oversampling Technique (SMOTE) was used to balance the data of the contraceptive implant failures. SMOTE resulted in better and more effective accuracy than other oversampling methods in handling the imbalance class because it reduced overfitting. The balanced data were then predicted using backpropagation neural networks. The prediction system was used to detect if a woman using a contraceptive implant was pregnant or not. This study used 300 data, consisting of 285 major data (not pregnant) and 15 minor data (pregnant). Of 300 data, two groups of data were formed: 270 training data and 30 testing data. Of 270 training data, 13 were minor training data and 257 were major training data. The minor training data in the training data were duplicated as much as the number of data in major classes so that the total training data became 514, consisting of 257 major data, 13 original minor data, and 244 artificial minor data. The prediction system resulted in an accuracy of 96.1% on the 500th and 1,000th epochs. The combination of SMOTE and Backpropagation Neural Network was proven to be able to make a good prediction result in imbalance class.

scholarly journals Neural Computation based General Disease Prediction Model

2019 ◽  
Vol 8 (2) ◽  
pp. 5646-5649 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Pattern Mining ◽  
Disease Prediction ◽  
Prediction System ◽  
Computing Technique ◽  
Soft Computing Technique ◽  
Artificial Neural ◽  
Prediction Techniques

To predict the patient disease using soft computing technique is the primary motto of the disease prediction system. Currently, researchers are trying to develop a disease prediction system using pattern mining technique. Here, a technique for disease prediction system using genetic algorithm and artificial neural network is proposed. The genetic algorithm is used for mining the most occurrences of disease sequences rules. To form the disease prediction system, the best rule which is obtained by means of genetic algorithm is used. Artificial neural network is trained to predict the disease. Accuracy of disease prediction is compared with other prediction techniques

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