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Diabetes Prediction Using Feature Selection

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Neural network is one of the pattern classification techniques, which has been widely used in many fields application. The multilayer perceptron in a training process has an accuracy impact. The selection of features is another factor that influences classification accuracy. The objective of this research is to optimize simultaneously parameters and subset of functionalities in order to increase multilayer perceptron accuracy. A genetic algorithm approach is presented in order to characterize selection and optimization parameters that serve as input to the multilayer perceptron classifier. The proposed algorithm can scan irrelevant information in PIMA database and obtain better accuracy. The results of this experience show that the proposed algorithm can provide a significant performance gain in terms of accuracy and training speed for the multilayer perceptron classification.
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Diabetes Mellitus (DM); PIMA Database; MLP Neural Networks; Genetic Algorithm; Selection of Variables

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S. Nagarajan, R.M. Chandrasekaran, P. Ramasubramanian, Data Mining Techniques for Performance Evaluation of Diagnosis in gestational Diabetes. IJCRAR, volume 2, Pages 91–98, 2014.

J. Steffi, R. Balasubramanian, K Aravind Kumar, Predicting Diabetes Mellitus using Data Mining Techniques: Comparative analysis of Data Mining Classification Algorithms. Int. J. Eng. Dev. Res. Volume 6, Pages 460–467, 2018.

A. Z. Woldaregay, E. Årsand, T. Botsis, D. Albers, L. Mamykina, G. Hartvigsen, Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes, J Med Internet Res, volume 21, Issue 5, 2019.

S.Perveen, M. Shahbaz, A. Guergachi and K Keshavjee, Performance Analysis of Data Mining Classification Techniques to Predict Diabetes, Procedia Computer Science, volume 82, Pages 115-121, 2016.

C. Fiarni, E. M. Sipayung, S. Maemunah, Analysis and Prediction of Diabetes Complication Disease using Data Mining Algorithm, Procedia Computer Science, volume 161, Pages 449-457, 2019.

P.S. Kumar, V Umatejaswi, Diagnosing Diabetes using Data Mining Techniques International, Journal of Scientific and Research Publications, volume 7, Pages 705-709, 2017.

A.M. Al-Khasawneh, Decision Support System for Diabetes Classification Using Data Mining Techniques: Classification Using Data Mining Techniques, Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics, 2018, ISBN13:9781522554608.

C. Wenqian, C. Shuyu Chen, Z. Hancui, W. Tianshu, A hybrid prediction model for type 2 diabetes using K-means and decision tree, 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017.

H. Kaur, E. Lechman, A. Marszk, Catalyzing Development through ICT Adoption: The Developing World Experience, Springer Publishers, Switzerland, 2017.

D. Jain, V. Singh Feature selection and classification systems for cronic disease prediction: A review, Egyptian Informatics Journal, Volume 19, Issue 3, Pages 179-189, 2018.

V. Anuja Kumari, R. Chitra, Classification of diabetes disease using support vector machine. Int. J. Eng. Res. Appl, Volume 3, Issue 2, Pages 1797–1801, 2013.

D. K. Choubey, S. Tripathi, P. Kumar, V. Shukla, V. K. Dhandhania, Classification of Diabetes Patients Using Kernel Based Support Vector Machines, Recent Advances in Computer Science and Communications, volume 14, Issue 4, Pages 1245 -1258, 2021.

G. A. Pethunachiyar, Classification of Diabetes Patients Using Kernel Based Support Vector Machines, International Conference on Computer Communication and Informatics (ICCCI), 2020.

N. M. Gail, A. Carpenter, Artmap-ic and medical diagnosis: Instance counting and inconsistent cases. Center for Adaptative Systems and Department of Cognitive and Neural Systems, volume 1, Pages 323–33, 1998.

T. Y. Kamer, Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks. Yildiz Technical University, Department of Electronics and Comm. Eng, 2003.

F. Beloufa, M.A. Chikh, Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm. Comput. Methods Progr. Biomed, volume 112, Pages 92–103, 2013.

K. Chari, M.C. Babu, S. Kodati, Classification of Diabetes using Random Forest with Feature Selection Algorithm. Int. J. Innovative Technol. Exploring Eng, volume 9, Pages 1295–1300, 2019.

A. Ali, M. A. T. Alrubei, L. F. M. Hassan, M. A. M. Al-Ja'afari, S. H. Abdulwahed, Diabetes Diagnosis Based On Knn, IIUM Engineering Journal, volume 21, Issue 1, 2020.

D. Krati Saxena, Z. Khan, S. Singh, Diagnosis of diabetes mellitus using k nearest neighbor algorithm. International Journal of Computer Science Trends and Technology (IJCST), volume 2, Issue 4, Pages 36-43. 2014.

I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, I. Chouvarda, Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, volume 15, Pages 104-116, 2017.

E. S. Kilpatrick, A. S. Rigby, S. L. Atkin, Mean blood glucose compared with HbA 1c in the prediction of cardiovascular disease in patients with type 1 diabetes. Diabetologia, volume 51, Issue 2, Pages 365-371, 2008.

B. M. Patil, R. C. Joshi, D. Toshniwal, Hybrid prediction model for type-2 diabetic patients. Expert systems with applications, volume 37, Issue 12, Pages 8102-8108, 2010.

M. A. Abdul-Ghani, V. Lyssenko, T. Tuomi, R. A. DeFronzo, L. Groop, Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study. Diabetes care, volume 32, Issue 2, Pages 281-286, 2009.

O. Ebru Pekel, O. Tuncay , Diagnosis of diabetes mellitus sing artificial neural network and classification and regression tree optimized with genetic algorithm, Journal of Forecasting, January 2020.

J. B. Ali, T. Hamdi, N. Fnaiech, V. DI Costanzo, F. Fnaiec. J.M. Ginoux, Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network. Biocybernetics and Biomedical Engineering, volume 38, Issue 4, Pages 828-840, 2018.

M. Ramzan, Comparing and evaluating the performance of WEKA classifiers on critical diseases. In 2016 1st India International Conference on Information Processing (IICIP) Pages 1-4, 2016.

K. Saravananathan, T. Velmurugan, Analyzing Diabetic Data using Classification Algorithms in Data Mining. Indian Journal of Science and Technology, volume 9, Issue 43, 2016.

T. Santhanam, M. S. Padmavathi, Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis. Procedia Computer Science, volume 47, Pages 76-83, 2015.

M. Pradhan, A. Rahman, P. Acharya, R.Gawade, A. Pateria, Design of Classifier for Detection of Diabetes using Genetic Programming, International Conference on Computer Science and Information Technology Pattaya,2011.

A. A. Al Jarullah, Decision tree discovery for the diagnosis of type II diabetes. In 2011 International conference on innovations in information technology IEEE, Pages 303-307, 2011

Liu, B.; Li, Y.; Sun, Z.; Ghosh, S.; Ng, K. Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2–7 February 2018; pp. 101–108.

A.K. Dwivedi, Analysis of computational intelligence techniques for diabetes mellitus prediction. Neural Comput. Appl,volume 30, Pages 3837–3845, 2018.

P. Samant, R. Agarwal, Machine learning techniques for medical diagnosis of diabetes using iris images. Comput. Methods Progr. Biomed, volume 157, Pages 121–128, 2018.

V. V. Vijayan, C. Anjali, Prediction and diagnosis of diabetes mellitus A machine learning approach. In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) IEEE, Pages 122-127, 2015.

N.P. Tiggaa, S. Garg, Prediction of Type 2 Diabetes using Machine Learning Classification Methods, In Proceedings of the International Conference on Computational Intelligence and Data Science (ICCIDS), Gurgaon, India, 6–7 September 2019.

A. Viloria, Y.;Herazo-Beltran, D.;Cabrera, O.B. Pineda, Diabetes Diagnostic Prediction Using Vector Support Machines, Procedia Comput, volume 170, Pages 376–381, 2020.

H. Kaur, V. Kumari, Predictive modelling and analytics for diabetes using a machine learning approach. Appl. Comput. Inf. 2018.

J.O. Orukwo, L .G. Kabari, Diagnosing Diabetes Using Artificial Neural Networks, Eur. J. Eng. Res. Sci, volume 5, Pages 221–224, 2020.

J. B. Ali, T. Hamdi, N. Fnaiech, V. DI Costanzo, F. Fnaiec. J.M. Ginoux, Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network. Biocybernetics and Biomedical Engineering, volume 38, Issue 4, Pages 828-840, 2018.

J. M. Ginoux, H. Ruskeepää, M. Perc, R. Naeck, V. Di Costanzo, M. Bouchouicha, F. Fnaiech, M. Sayadi, T. Hamdi, Is type 1 diabetes a chaotic phenomenon?, Chaos, Solitons & Fractals, volume 111, Pages 198-205, 2018.

O. I. Horyn, H. I. Falfushynska1 , L. L. Gnatysh yna1, B. B. Buyak , N. I. Rusnak , O. O. Fedoruk , O. B. Stoliar, Carassius auratus As a novel model for the hyperglycemia study, Ukr. Biochem. J., Volume 91, Issue 4, 2019.

A. Aliberti, I. Pupillo, S. Terna, E. Macii, S. Di Cataldo, A Multi-Patient Data Driven Approach to Blood Glucose Prediction, IEEE Access, volume 7, Pages 69311-69325, 2019.

Y. Jing Fan, G., Xun, A. Naveed, C. Kelley, P.F.B. Nelson, I. Faramar, A. Michael, S. Michael, Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin, Diabetes 2020 volume 69, Issue 8, Pages 1815-1826, 2020.

F. Mercaldo, V. Nardone, A. Santone, Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Procedia Computer Science, volume 112, Pages 2519-2528, 2017.

M. Alehegn, R. Raghvendra Joshi, P. Mulay, Diabetes Analysis And Prediction Using Random Forest, KNN, Naïve Bayes, And J48: An Ensemble Approach, International Journal of Scientific & Technology Research, volume 8, Issue 9, 2019.

R. Ali, M.H. Siddiqi, M. Idris, B.H. Kang, S. Lee, Prediction of Diabetes Mellitus Based on Boosting Ensemble Modeling. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Belfast, UK, 2–5 Springer: Cham, Switzerland, Pages 25–28, December 2014.

N. Nai-arun, R. Moungmai, Comparison of classifiers for the risk of diabetes prediction. Procedia Computer Science, volume 69, Pages 132-142, 2015.

S. Aishwarya, S. Anto, A Medical Expert System based on Genetic Algorithm and Extreme Learning Machine for Diabetes Disease Diagnosis. Int. J. Sci. Eng. Technol, volume 3, Pages 1375–1380, 2014.

W. Zhu, P. Zhong, A new one-class SVM based on hidden information. Knowl. Based Syst, volume 60, Pages 35–43. 2014.

Lee, C.-S.; Wang, M.-H. A fuzzy expert system for diabetes decision support application. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2011, 41, 139–153.

V. Jain, S. Raheja, Improving the Prediction Rate of Diabetes using Fuzzy Expert System. Int. J. Inf. Technol. Comput. volume 10, Pages 84–91, 2015.

B. J. Lee, B. Ku, J. Nam, D. D. Pham, J. Y. Kim, Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes. IEEE Journal of Biomedical and Health Informatics, volume 18, Issue 2, Pages 555-561, 2014.

F. Rosenblatt, The Perceptron: A Theory of Statistical Separability in Cognitive Systems, Cornell Aeronautical Laboratory, Report N°. VG1196-G-1, January, 1958.

V. Rozinajova, A. Bou Ezzeddine, M. Loderer, J. Loebl, R. Magyar, P. Vrablecova. Computational Intelligence in Smart Grid Environment, Intelligent Data-Centric Systems, pages 23-59, 2018.

A. Pavate, N. Ansari, Risk prediction of disease complications in type 2 diabetes patients using soft computing techniques. In 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), Pages 371-375, 2015.

R. W. Grant, M. Hivert, J. C. Pandiscio, J. C. Florez, Nathan, D. M., J. B. Meigs, The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia, volume 52, Issue 11, Pages 2299-2305, 2009.

Pima Indians Diabetes Data. [(accessed on 23 June 2020)]. Available online:

B. Sarojini, N. Ramaraj, Enhancing Medical Prediction using Feature Selection. International Journal of Artificial Intelligence & Expert Systems (IJAE), volume 1, Issue 3, 2011.

M. Pradhan, R. K. Sahu, Predict the onset of diabetes disease using Artificial Neural Network (ANN), International Journal of Computer Science & Emerging Technologies, volume 2, Issue 2, April 2011.

M. Pradha, K. Kohale, P. Naikade, A. Pachore, E. Palwe, Design of Classifier for Detection of Diabetes using Neural Network and Fuzzy k-Nearest Neighbor Algorithm, International Journal of Computational Engineering Research (IJCER), volume 2, Issue 5, 2012.

C. Zecchin, A. Facchinetti, G. Sparacino and C. Cobelli, How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study, Journal of Diabetes Science and Technology, volume 10, Issue 5, pages 1149-1160, 2016.

Jumaa Alkurawy, L., Saleh, M., Humood, K., Modeling, Identification and Control of Inverse Kinematic of PUMA Robots, (2020) International Journal on Engineering Applications (IREA), 8 (4), pp. 140-147.

Monadjemi, S., Moallem, P., Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach, (2018) International Journal on Engineering Applications (IREA), 6 (1), pp. 29-34.

K.C. Tan, E.J. Teoh, Q. Yua, K.C. Goh, A hybrid evolutionary algorithm for attribute selection in data mining, Expert systems with applications, volume 36, Pages 8616-8630, 2009.

S. Anto, S. Chandramathi, An expert system based on SVM and hybrid ga-sa optimization for hepatitis diagnosis, International Journal of Computer Engineering in Research Trends, volume 2, issue 7, Pages 437-443, 2015.

M. A. Elaziz, S. Mirjalili, A hyper-heuristic for improving the initial population of whale optimization algorithm, Knowledge-Based Systems, volume 172, Pages 42-63, 2019.

M. Pradhan, A. Rahman, P. Acharya, R.Gawade, A. Pateria, Design of Classifier for Detection of Diabetes using Genetic Programming, International Conference on Computer Science and Information Technology Pattaya, 2011.

T. Hamdi, J. Ben Ali, V. Di Costanzo, F. Fnaiech, E. Moreau, J. Ginoux, Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm, Biocybernetics and Biomedical Engineering, volume 38, Pages 362-372, 2018.

Rebro, M., Tárník, M., Murgaš, J., Glycemia Prediction Accuracy of Simple Linear Models with Online Parameter Identification, (2016) International Review on Modelling and Simulations (IREMOS), 9 (5), pp. 367-373.

Tárník, M., Bátora, V., Ludwig, T., Ottinger, I., Miklovičová, E., Murgaš, J., Prediction of Glycemia Based on Diabetes Self-Monitoring Data, (2015) International Review of Automatic Control (IREACO), 8 (2), pp. 113-119.

Ilka, A., Ottinger, I., Ludwig, T., Tárník, M., Veselý, V., Miklovičová, E., Murgaš, J., Robust Controller Design for T1DM Individualized Model:Gain-Scheduling Approach, (2015) International Review of Automatic Control (IREACO), 8 (2), pp. 155-162.

J. Li, O. Arandjelovic, Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning, in: IEEE EMBS Int. Conf. on Biomed. & Health Informat. (BHI), Orlando, Pages 357–360, 2017.

A. Ali, S.M. Shamsuddin, A.L. Ralescu, Classification with class imbalance problem: a review, Int. J. Adv. Soft Comput. App, volume 5, Issue 3, Pages 176–204, 2013.

S.Mishra, K. Hrudaya, P. K. Mallick, A. K. Bhoi, P. Barsocchi, EAGA-MLP-An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis, Sensors (Basel), volume 14, Jul 2020.

M. Komi, J. Li, Y. Zhai, X. Zhang, Application of data mining methods in diabetes prediction. In 2017 2nd International Conference on Image, Vision and Computing (ICIVC) IEEE. Pages 1006-1010, 2017.

A. S. Rani, S. Jyothi, Performance analysis of classification algorithms under different datasets. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) IEEE, Pages 1584-1589, 2016.

X. H. Meng, Y. X. Huang, D. P. Rao, Q. Zhang, Q. Liu, Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung Journal of Medical Sciences, volume 29, Issue 2, Pages 93-99, 2013.

S. Kang, P. Kang, T. Ko, S. Cho, S. J. Rhee, K. S. Yu, An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. Expert Systems with Applications, volume 42, Issue 9, Pages 4265-4273, 2015.

Y. Guo, G. Bai, Y. Hu, Using bayes network for prediction of type-2 diabetes. In 2012 International Conference for Internet Technology and Secured Transactions IEEE, Pages 471-472, 2012.

E. K. Hashi, M. S. U. Zaman, M. R. Hasan, An expert clinical decision support system to predict disease using classification techniques. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) IEEE, Pages 396-400, 2017.

S. Deepti, S. Dilip Singh, Prediction of Diabetes using Classification Algorithms, International Conference on Computational Intelligence and Data Science (ICCIDS 2018) Volume 132, Pages 1578-1585, 2018.

A. Negi, V. Jaiswal, A first attempt to develop a diabetes prediction method based on different global datasets. In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) IEEE, Pages 237-241, 2016.

M. Kalpana, A.V Senthil, Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism, International Journal Advanced Networking and Applications, volume: 03, Issue 02, Pages 1128-1134, 2011.

E.P. Ephzibah, Cost-Effective Approach on Attribute Selection Using Genetic Algorithms and Fuzzy Logic for Diabetes Diagnosis. Int. J. Soft Comput, volume 2, Pages 86–99, 2011.

S. Sapna, A. Tamilarasi, M. Pravin Kumar, Implementation of Genetic Algorithm in Predicting Diabetes. Int. J. Comput. Volume 9, Pages 234–240, 2012.

V. V. Kamadi, A. R. Allam, S. M. Thummala, A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach. Applied Soft Computing, volume 49, Pages137-145, 2016.

E. Dogantekin, A. Dogantekin, D. Avci, L. Avci, An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS. Digital Signal Processing, volume 20, Issue 4, Pages. 1248-1255, 2010.

C.l. Huang, C.j. Wang, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems With Applications, volume 31, Pages 231-240, 2006.

R. Sarojini balakrishnan, Features selection using fcbf in type ii diabetes databases, International Conference on it to celebrate s.charmonman’s 72"d Birthday, Thailand, Pages 50-58, 2009.

L. Han , S. Luo, J. Yu, Rule Extraction from Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes. IEEE Journal of Biomedical & Health Informatics, volume 19, Issue 2, 2015.

J. Sahoo, M. Dash, A. Pati, Diabetes Prediction Using Machine Learning Classification Algorithms, International Research Journal of Engineering and Technology (IRJET) Volume 7, Issue 8, 2020.

F. Pociot et A. Lernmark, Genetic risk factors for type 1 diabetes, The Lancet, volume. 387, n° 110035, Pages 2331-2339, 2016.

S. Park, D. Choi, M. Kim, W. Cha, C. KIM, I. C. Moon, Identifying prescription patterns with a topic model of diseases and medications, Journal of Biomedical Informatics, volume 75, Pages 35-47, November 2017.

L. Mamykina, E. M Heitkemper, A. Smaldone, R. Kukafka, Personal Discovery in Diabetes Self-Management: Discovering Cause and Effect Using Self-Monitoring Data, Journal of Biomedical Informatics, volume 76, 2017.

K. Papatheodorou, M. Banach, M. Edmonds, N. Papanas, D. Papazoglou, Complications of Diabetes, Journal of Diabetes Research, Pages 1-5, 2015 .

M. B. Schulze, C. Weikert, T. Pischon, M. M. Bergmann, H. Al-Hasani, E. Schleicher, H. G. Joost, Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes care, volume 32, Issue 11, Pages 2116- 2119, 2009.

J.L. Fernandez, J. M. Carrillo, M. Hosni, A. Idri, G. Garcia, Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review, Annu Int Conf IEEE Eng Med Biol Soc. Pages 3956-3959, 2019.

T. Zheng, W. Xie, L. Xu, X. He, Y. Zhang, M. You, Y. Chen, A machine learning-based framework to identify type 2 diabetes through electronic health records. International Journal of Medical Informatics, volume 97, Pages 120-127, 2017.

K. Ateeq, G. Ganapathy, The novel hybrid Modified Particle Swarm Optimization Neural Network (MPSO-NN) Algorithm for classifying the Diabetes, International Journal of Computational Intelligence Research, volume 13, Issue 4, Pages 595-614, 2017.

Boukef, H., Benrejeb, M., Borne, P., Genetic Algorithm and Based Particle Swarm Optimization Comparison for Solving a Flow-Shop Multiobjective Scheduling Problem in Pharmaceutical Industries, (2018) International Journal on Engineering Applications (IREA), 6 (6), pp. 221-226.

A. Sathe, T. Meressi, Optimizing Basal Insulin Delivery for Patients Suffering from Type I Diabetes Mellitus Using Particle Swarm Optimization Algorithm, Journal of Bioengineering and Biomedical Science, volume 8, Issue 4, 2018.

J. Beschi Raja , S. Chenthur Pandian, PSO-FCM based data mining model to predict diabetic disease, Computer Methods and Programs in Biomedicine, volume 196, 2020.

J. Zhang, Z, Ma, Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO), Computational Intelligence And Neuroscience.

S. Duran-Garcia, J. Lee, H. Yki-Jarvinen, J. Rosenstock, U. Hehnke, S. Thiemann, S.Patel, HJ. Woerle. Efficacy and safety of linagliptin as add-on therapy to basal insulin and metformin in people with Type 2 diabetes. Diabet Med, volume 33, Pages 926-933, 2016.

T. Shu, B. Zhang, Y.Y. Tang, An improved non invasive method to detect Diabetes Mellitus using the Probabilistic Collaborative . Representation based Classifier. Inf. Sci., volume 467, Pages 477–488, 2018.

Liu, B.; Li, Y.; Sun, Z.; Ghosh, S.; Ng, K. Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2–7 February 2018; pp. 101–108.

I. Rodríguez-Rodríguez , J.V. Rodríguez, J.M. Molina-García-Pardo, M.-Á. Zamora-Izquierdo, M.T, Martínez-Inglés A Comparison of Different Models of Glycemia Dynamics for Improved Type 1 Diabetes Mellitus Management with Advanced Intelligent Analysis in an Internet of Things Context. Appl, volume 10, 2020.

Y. Liu, Artificial Intelligence–Based Neural Network for the Diagnosis of Diabetes: Model Development. JMIR Med. Inf, volume 8, e18682, 2020.

A. Tarik, S.M.A. Rashid, R.M., Abdullah, An Intelligent Approach for Diabetes Classification, Prediction and Description. Advances in Intelligent Systems and Computing 424, Pages 323–335, 2016.


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