[Frontiers in Bioscience, Landmark, 25, 1202-1229, March 1, 2020]

An AI-based approach in determining the effect of meteorological factors on incidence of malaria

Ajeet Kumar Verma1, Venkatanareshbabu Kuppili1, Saurabh K. Srivastava2, Jasjit S. Suri2,3

1Department of Computer Science and Engineering, NIT Goa, India, 2Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA, 3AtheroPoint™, Roseville, CA, USA

TABLE OF CONTENTS

1. Abstract
2. Introduction
3. Related works
4. Factors influencing malaria
    4.1. Mosquito species
      4.1.1. Anopheles mosquito
      4.1.2. Aedes mosquito
      4.1.3. Culex mosquito
    4.2. Climatic conditions
    4.3. Humans and households
5. Classifiers used in this study
    5.1 Non-spiking classifiers
      5.1.1. Cosine k-nearest neighbor (k-NN)
      5.1.2. Linear support vector machines (L-SVM)
      5.1.3. Neural Time Series (NTS)
      5.1.4. Decision tree (DT)
      5.1.5. Random forest (RF)
      5.1.6. Multilayer perceptron (MLP)
    5.2. Spiking classifiers
      5.2.1. Integrate and fire neuron (IFN)
      5.2.2. Quadratic integrate and fire neuron (QIFN)
6. Material and methods
    6.1. Dataset description
      6.1.1. Dataset
      6.1.2. Extended dataset
      6.1.3. Ethics approval
    6.2. Experimental protocols
      6.2.1. Experiment 1: Application of non-spiking classifiers over MD1 and MD2 datasets
      6.2.2. Experiment 2: Application of spiking classifiers over MD1 and MD2 datasets
    6.3. Statistics
7. Results and validation
    7.1. Results of experiment 1
      7.1.1. Results for Linear SVM
      7.1.2. Results for Neural time series
      7.1.3. Results for Cosine k-NN
      7.1.4. Results for decision tree
      7.1.5. Results for Random forest
      7.1.6. Results for Multilayer perceptron
    7.2. Results of experiment 2
      7.2.1. Results for the Integrate and fire neuron
      7.2.2. Results for the Quadratic integrate and fire neuron model
    7.3. Validation
      7.3.1. Receiver operating characteristic analysis
      7.3.2. Regression analysis
      7.3.3. Training states and error
8. Discussion
    8.1. Influence on Feature enrichment
    8.2. A note on quadratic functions
    8.3. Strengths, weaknesses, and extensions
9. Conclusions
10. Acknowledgments
11. References

1. ABSTRACT

This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers including Quadratic Integrate and Fire neuron (QIFN) model as a benchmark. As per research studies, parasite transmission is highly dependent on the (i) stagnant water, (ii) population of area and the (iii) greenery of the locality. Considering these factors, three more attributes were added to the existing novel dataset and comparison on the results is presented. For four feature dataset, QIFN exhibited an accuracy of 97.08% in K10 protocol, and with extended dataset; QIFN yields an accuracy of 99.58% in K10 protocol. The benchmarking results showed reliability and stability. There is 12.47% improvement against multilayer perceptron (MLP) and 5.39% against integrate-and-fire neuron (IFN) model. The QIFN model performed the best over the conventional classifiers for deciphering the risk of acquiring malaria in different geographical regions worldwide.

11. REFERENCES

1. Rao P. V. S.: Fifth Generation Computers and Artificial Intelligence. IETE J Res 34, no. 3: 159-165 (1988)
DOI: 10.1080/03772063.1988.11436725

2. Neural Networks: https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/History/history2.html, last accessed on 19-08-2019 (2019)

3. Sporns Olaf.: Structure and function of complex brain networks. Dialogues Clin Neurosci 15, no. 3: 247 (2013)

4. McCulloch Warren S., and Walter Pitts: A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 5, no. 4: 115-133 (1943)
DOI: 10.1007/BF02478259

5. Du Zidong, Daniel D. Ben-Dayan Rubin, Yunji Chen, Liqiang Hel, Tianshi Chen, Lei Zhang, Chengyong Wu, and Olivier Temam: Neuromorphic accelerators: A comparison between neuroscience and machine-learning approaches. In 2015 48th Annual IEEE/ACM MICRO, pp. 494-507. IEEE (2015)
DOI: 10.1145/2830772.2830789

6. Hodgkin Alan L., and Andrew F. Huxley: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. (Lond.) 117, no. 4: 500-544 (1952)
DOI: 10.1113/jphysiol.1952.sp004764

7. Squire Larry, Darwin Berg, Floyd E. Bloom, Sascha Du Lac, Anirvan Ghosh, and Nicholas C. Spitzer, eds.: Fundamental neuroscience. Academic Press (2012)

8. Maass Wolfgang: Networks of spiking neurons: the third generation of neural network models. Neural networks 10, no. 9: 1659-1671 (1997)
DOI: 10.1016/S0893-6080(97)00011-7

9. Meftah Boudjelal, Olivier Lézoray, Soni Chaturvedi, Aleefia A. Khurshid, and Abdelkader Benyettou: Image processing with spiking neuron networks. In Artificial Intelligence, Evolutionary Computing and Metaheuristics, pp. 525-544. Springer, Berlin, Heidelberg (2013)
DOI: 10.1007/978-3-642-29694-9_20

10. Wu Jibin, Yansong Chua, Malu Zhang, Haizhou Li, and Kay Chen Tan: A spiking neural network framework for robust sound classification. Front. Neurosci. 12 (2018)
DOI: 10.3389/fnins.2018.00836

11. Long Jonathan, Evan Shelhamer, and Trevor Darrell: Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440 (2015)
DOI: 10.1109/CVPR.2015.7298965

12. Ugon Adrien, Daniel Karlsson, and Gunnar O. Klein, eds. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created EHealth. IOS Press Vol. 247 (2018)

13. Turgut Siyabend, Mustafa Dağtekin, and Tolga Ensari: Microarray breast cancer data classification using machine learning methods. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-3. IEEE (2018)
DOI: 10.1109/EBBT.2018.8391468

14. National Diabetes Data Group: Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28, no. 12: 1039-1057 (1979)
DOI: 10.2337/diab.28.12.1039

15. Martis Roshan Joy, U. Rajendra Acharya, Choo Min Lim, and Jasjit S. Suri: Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl.-Based Syst. 45: 76-82 (2013)
DOI: 10.1016/j.knosys.2013.02.007

16. Suri Jasjit S., Chirinjeev Kathuria, and Filippo Molinari, eds: Atherosclerosis disease management. Springer Science & Business Media (2010)
DOI: 10.1007/978-1-4419-7222-4

17. Acharya U. Rajendra, Muthu Rama Krishnan Mookiah, S. Vinitha Sree, David Afonso, Joao Sanches, Shoaib Shafique, Andrew Nicolaides, Luís Mendes Pedro, J. Fernandes e Fernandes, and Jasjit S. Suri: Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Comput Meth Prog Bio 51, no. 5: 513-523 (2013)
DOI: 10.1007/s11517-012-1019-0

18. Cuadrado-Godia Elisa, Ankush D. Jamthikar, Deep Gupta, Narendra N. Khanna, Tadashi Araki, Md Maniruzzaman, Luca Saba, Nicolaides, A., Sharma, A., Omerzu, T. and Suri, H.S.: Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach. Comput Biol Med. 108: 182-195 (2019)
DOI: 10.1016/j.compbiomed.2019.03.020

19. El-Baz Ayman, Georgy Gimel'farb, Robert Falk, M. Abo El-Ghar, and Jasjit Suri: Appearance analysis for the early assessment of detected lung nodules. Lung imaging and computer aided diagnosis 17: 395-404 (2011)
DOI: 10.1201/b11106-18

20. Tandel Gopal S., Mainak Biswas, Omprakash G. Kakde, Ashish Tiwari, Harman S. Suri, Monica Turk, John R. Lair, J.R., Asare, C.K., Ankrah, A.A., Khanna, N.N. and Madhusudhan, B. K.: A review on a deep learning perspective in brain cancer classification. Cancers 11, no. 1: 111 (2019)
DOI: 10.3390/cancers11010111

21. Suri J. S., Ruey-Feng Chang, G. A. Giraldi, and P. S. Rodrigues: Non-extensive entropy for cad systems of breast cancer images. In 2006 19th Brazilian Symposium on Computer Graphics and Image Processing, pp. 121-128. IEEE (2006)
DOI: 10.1109/SIBGRAPI.2006.31

22. Saba Luca, Nilanjan Dey, Amira S. Ashour, Sourav Samanta, Siddhartha Sankar Nath, Sayan Chakraborty, João Sanches, Dinesh Kumar, RuiTato Marinho, and Jasjit S. Suri: Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm. Comput Meth Prog Bio 130: 118-134 (2016)
DOI: 10.1016/j.cmpb.2016.03.016

23. Acharya U. Rajendra, S. Vinitha Sree, Ricardo Ribeiro, Ganapathy Krishnamurthi, Rui Tato Marinho, João Sanches, and Jasjit S. Suri: Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med. Phys. 39, no. 7Part1: 4255-4264 (2012)
DOI: 10.1118/1.4725759

24. Rajendra Acharya U., Jos AE Spaan, Jasjit S. Suri, and Shankar M. Krishnan: Advances in cardiac signal processing. Springer-Verlag Berlin and Heidelberg & Company KG (2007)
DOI: 10.1007/978-3-540-36675-1

25. Shrivastav Vimal K., Narendra D. Londhe, Rajendra S. Sonawane, and Jasjit S. Suri: Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Comput Meth Prog Bio 126: 98-109 (2016)
DOI: 10.1016/j.cmpb.2015.11.013

26. Quackenbush John: Microarray analysis and tumor classification. NEJM 354, no. 23: 2463-2472 (2006)
DOI: 10.1056/NEJMra042342

27. Tsai Meng-Hsiun, Shyr-Shen Yu, Yung-Kuan Chan, and Chun-Chu Jen: Blood smear image based malaria parasite and infected-erythrocyte detection and segmentation. J Med Syst. 39, no. 10: 118 (2015)
DOI: 10.1007/s10916-015-0280-9

28. Araki Tadashi, Nobutaka Ikeda, Devarshi Shukla, Pankaj K. Jain, Narendra D. Londhe, Vimal K. Shrivastava, Sumit K. Banchhor, Saba, L., Nicolaides, A., Shafique, S. and Laird, J.R: PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. Comput Meth Prog Bio 128: 137-158 (2016)
DOI: 10.1016/j.cmpb.2016.02.004

29. Banchhor Sumit K., Narendra D. Londhe, Tadashi Araki, Luca Saba, Petia Radeva, John R. Laird, and Jasjit S. Suri: Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med. 91: 198-212 (2017)
DOI: 10.1016/j.compbiomed.2017.10.019

30. Revich Boris, Nikolai Tokarevich, and Alan J. Parkinson: Climate change and zoonotic infections in the Russian Arctic. Int. J. Circumpolar Health 71, no. 1:18792 (2012)
DOI: 10.3402/ijch.v71i0.18792

31. Zinszer Kate, Ruth Kigozi, Katia Charland, Grant Dorsey, Timothy F. Brewer, John S. Brownstein, Moses R. Kamya, and David L. Buckeridge: Forecasting malaria in a highly endemic country using environmental and clinical predictors. Malaria J. 14, no. 1: 245 (2015)
DOI: 10.1186/s12936-015-0758-4

32. Shone Scott M., Frank C. Curriero, Cyrus R. Lesser, and Gregory E. Glass: Characterizing population dynamics of Aedes sollicitans (Diptera: Culicidae) using meteorological data. J Med Entomol 43, no. 2: 393-402 (2014)
DOI: 10.1093/jmedent/43.2.393

33. Barrett Meredith A., Olivier Humblet, Robert A. Hiatt, and Nancy E. Adler: Big data and disease prevention: from quantified self to quantified communities. Big data 1, no. 3: 168-175 (2013)
DOI: 10.1089/big.2013.0027

34. Ahumada Jorge A., Dennis Lapointe, and Michael D. Samuel: Modeling the population dynamics of Culex quinquefasciatus (Diptera: Culicidae), along an elevational gradient in Hawaii. J Med Entomol 41, no. 6: 1157-1170 (2004)
DOI: 10.1603/0022-2585-41.6.1157

35. Sahoo G. B., S. G. Schladow, and J. E. Reuter: Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. J. Hydrol. 378, no. 3-4: 325-342 (2009)
DOI: 10.1016/j.jhydrol.2009.09.037

36. Dopazo Joaquín, Huaichun Wang, and José María Carazo: A new type of unsupervised growing neural network for biological sequence classification that adopts the topology of a phylogenetic tree. In International Work-Conference on Artificial Neural Networks, pp. 932-941. Springer, Berlin, Heidelberg (1997)
DOI: 10.1007/BFb0032553

37. Das Nani Gopal, Sunil Dhiman, Pranab Kumar Talukdar, Diganta Goswami, Bipul Rabha, Indra Baruah, and Vijay Veer: Role of asymptomatic carriers and weather variables in persistent transmission of malaria in an endemic district of Assam, India. Infection ecology & epidemiology 5, no. 1: 25442 (2015)
DOI: 10.3402/iee.v5.25442

38. Devi N. Pemola, and R. K. Jauhari: Climatic variables and malaria incidence in Dehradun, Uttaranchal, India. J Vector Dis. 43, no. 1: 21 (2006)

39. Zhang Pu, and Zhongshi He: Using data-driven feature enrichment of text representation and ensemble technique for sentence-level polarity classification. J. Inf. Sci. 41, no. 4: 531-549 (2015)
DOI: 10.1177/0165551515585264

40. Cataltepe Zehra, Abdullah Sonmez, and Baris Senliol: Feature enrichment and selection for transductive classification on networked data. Pattern Recognit. Lett. 37: 41-53 (2014)
DOI: 10.1016/j.patrec.2013.07.009

41. Wang Peng, Heng Zhang, Bo Xu, Chenglin Liu, and Hongwei Hao: Short text feature enrichment using link analysis on Topic-Keyword graph. In Natural Language Processing and Chinese Computing, pp. 79-90. Springer, Berlin, Heidelberg (2014)
DOI: 10.1007/978-3-662-45924-9_8

42. Huang Ruili, Noel Southall, Menghang Xia, Ming-Hsuang Cho, Ajit Jadhav, Dac-Trung Nguyen, James Inglese, Raymond R. Tice, and Christopher P. Austin: Weighted feature significance: a simple, interpretable model of compound toxicity based on the statistical enrichment of structural features. ToxSci. 112, no. 2: 385-393 (2009)
DOI: 10.1093/toxsci/kfp231

43. Srivastava Abhishek, Osmar R. Zaïane, and Maria-Luiza Antonie: Feature space enrichment by incorporation of implicit features for effective classification. In 11th International Database Engineering and Applications Symposium (IDEAS), pp. 141-148. IEEE (2007)
DOI: 10.1109/IDEAS.2007.4318098

44. Sindagi Vishwanath, and Vishal Patel: DAFE-FD: Density Aware Feature Enrichment for Face Detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2185-2195. IEEE (2019)
DOI: 10.1109/WACV.2019.00236

45. Meigen J. W.: Wintermükke Trichocera: Systematische Beschreibung der bekannten europäischen zweiflügeligen Insekten I Aachen 22 (1818): 211-220 (2014)
DOI: 10.5962/bhl.title.13731

46. Perkins Susan L: Species concepts and malaria parasites: detecting a cryptic species of Plasmodium. Proc. Royal Soc. Lond. Series B: Biological Sciences 267, no. 1459: 2345-2350 (2000)
DOI: 10.1098/rspb.2000.1290

47. Colless D. H.: Notes on the Culicine Mosquitoes of Singapore: VII.-Host Preferences in Relation to the Transmission of Disease. Ann Trop Med PH 53, no. 3: 259-267 (1959)
DOI: 10.1080/00034983.1959.11685923

48. Mousson Laurence, Catherine Dauga, Thomas Garrigues, Francis Schaffner, Marie Vazeille, and Anna-Bella Failloux: Phylogeography of Aedes (Stegomyia) aegypti (L.) and Aedes (Stegomyia) albopictus (Skuse)(Diptera: Culicidae) based on mitochondrial DNA variations. Genet. Res. (Camb.) 86, no. 1: 1-11 (2005)
DOI: 10.1017/S0016672305007627

49. Harbach Ralph E.: Classification within the cosmopolitan genus Culex (Diptera: Culicidae): The foundation for molecular systematics and phylogenetic research. Acta Trop. 120, no. 1-2: 1-14 (2011)
DOI: 10.1016/j.actatropica.2011.06.005

50. Schlagenhauf-Lawlor Patricia: Travelers' malaria. PMPH-USA (2008)

51. Parham Paul Edward, and Edwin Michael: Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, no. 5: 620-626 (2009)
DOI: 10.1289/ehp.0901256

52. World Health Organization: The world health report 2006: working together for health. WHO (2006)

53. Nkumama Irene N., Wendy P. O'Meara, and Faith HA Osier: Changes in malaria epidemiology in Africa and new challenges for elimination. Trends Parasitol 33, no. 2: 128-140 (2017)
DOI: 10.1016/j.pt.2016.11.006

54. Altman Naomi S: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, no. 3: 175-185 (1992)
DOI: 10.1080/00031305.1992.10475879

55. Cortes Corinna, and Vladimir Vapnik: Support-vector networks. Mach. Learn. 20, no. 3: 273-297 (1995)
DOI: 10.1007/BF00994018

56. Srivastava Saurabh Kumar, Sandeep Kumar Singh, and Jasjit S. Suri: Healthcare Text Classification System and its Performance Evaluation: A Source of Better Intelligence by Characterizing Healthcare Text. J. Med. Syst. 42, no. 5: 97 (2018)
DOI: 10.1007/s10916-018-0941-6

57. Kuppili Venkatanareshbabu, Mainak Biswas, Aswini Sreekumar, Harman S. Suri, Luca Saba, Damodar Reddy Edla, Rui Tato Marinhoe, J. Miguel Sanches, and Jasjit S. Suri: Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. J. Med. Syst. 41, no. 10: 152 (2017)
DOI: 10.1007/s10916-017-0797-1

58. Srivastava Saurabh Kumar, Sandeep Kumar Singh, and Jasjit S. Suri: Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm. Comput Meth Prog Bio 172: 35-51 (2019)
DOI: 10.1016/j.cmpb.2019.01.011

59. Box George EP, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung: Time series analysis: forecasting and control. John Wiley & Sons (2015)

60. Cohen Mike X.: Analyzing neural time series data: theory and practice. MIT press, (2014)
DOI: 10.7551/mitpress/9609.001.0001

61. Utgoff Paul E.: Incremental induction of decision trees. Mach. Learn. 4, no. 2: 161-186 (1989)
DOI: 10.1023/A:1022699900025

62. Shrivastava Vimal K., Narendra D. Londhe, Rajendra S. Sonawane, and Jasjit S. Suri: A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Meth Prog Bio 150: 9-22 (2017)
DOI: 10.1016/j.cmpb.2017.07.011

63. Acharya U. Rajendra, Oliver Faust, Nahrizul Adib Kadri, Jasjit S. Suri, and Wenwei Yu: Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput Meth Prog Bio 43, no. 10: 1523-1529 (2013)
DOI: 10.1016/j.compbiomed.2013.05.024

64. Ho Tin Kam: Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, IEEE, vol. 1, pp. 278-282 (1995)
DOI: 10.1109/ICDAR.1995.598994

65. Breiman Leo: Random forests. Mach. Learn. 45, no. 1: 5-32 (2001)
DOI: 10.1023/A:1010933404324

66. Breiman Leo: Bagging predictors. Mach. Learn. 24, no. 2: 123-140. (1996)
DOI: 10.1007/BF00058655

67. Maniruzzaman Md, Md Jahanur Rahman, Benojir Ahammed, Md Menhazul Abedin, Harman S. Suri, Mainak Biswas, Ayman El-Baz, Petros Bangeas, Georgios Tsoulfas, and Jasjit S. Suri: Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Meth Prog Bio 176: 173-193 (2019)
DOI: 10.1016/j.cmpb.2019.04.008

68. Maniruzzaman Md, Md Jahanur Rahman, Md Al-Mehedi Hasan, Harman S. Suri, Md Menhazul Abedin, Ayman El-Baz, and Jasjit S. Suri: Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst. 42, no. 5: 92 (2018)
DOI: 10.1007/s10916-018-0940-7

69. Rumelhart David E., Geoffrey E. Hinton, and Ronald J. Williams.: Learning internal representations by error propagation. No. ICS-8506. UCSD La Jolla Inst for Cognitive Science, (1985)
DOI: 10.21236/ADA164453

70. Verma Ajeet K., Venkatanareshbabu Kuppili, Saurabh K. Srivastava, and Jasjit S. Suri: A new backpropagation neural network classification model for prediction of incidence of malaria. Front Biosci. (Landmark ed.) 25: 299-334 (2020)
DOI: 10.2741/4808

71. Abbott Larry F.: Lapicque's introduction of the integrate-and-fire model neuron (1907). Brain Res. 50, no. 5-6: 303-304. (1999)
DOI: 10.1016/S0361-9230(99)00161-6

Abbreviations: QIFN: Quadratic integrate and fire neuron model, MD1: Malaria Dataset 1, MD2: Malaria Dataset 2, HLL: high level languages, ICs: integrated circuits, ANN: Artificial Neural Network (ANN), AI: Artificial Intelligence, MCP: McCulloch Pitts model, R-C: Resistance-Capacitance, STDP: Spike-timing-dependent plasticity, SNN: Spiking Neural Network, IFN: Integrate and fire neuron, LSVM: Linear support vector machines, NTS: Neural time series, k-NN: k-nearest neighbour, DT: Decision tree, RF: Random forest, MLP: Multilayer perceptron, PI: Population Index, GI: Geographical Index, Gr I: Greenery Index

Key Words: Malaria, Quadratic Integrate-and-Fire Neuron, Spiking Neural Network, Machine Learning Algorithm, Artificial Neural Networks

Send correspondence to: Jasjit S. Suri, Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc. Roseville, CA, USA, Tel: 916-749-5628, Fax: 916-749-4942, E-mail: jsuri@comcast.net