[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


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


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.


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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