Malaria is usually transmitted by a female mosquito (7)(8). Malaria is also communicable through the bite of the mosquito. When, an infected Anopheles mosquito bites, it introduces parasite to our blood. Also, when a mosquito bites an infected person, biting mosquito becomes infected and it transmits the parasite. The parasite, present in Anopheles saliva, enters the blood through the bite. Through the bloodstream, parasite reaches to the liver. Parasite matures and reproduces in the liver within 48 to 72 hours (9)(10). The matured parasite then travels through the bloodstream, and starts infecting the blood cells, usually RBC. Once parasite enters into RBC, it starts multiplying within two to three days, causing the burst of a cell, and as a result, this infection is transmitted to other cells in the blood. At synchronous time intervals, infected blood cells burst and it introduces more population of parasites in the blood. The bursting cycle of infected blood cells is 48-72 hours. Each time when cells are bursting, a person feels a bout of fever, sweating and chills. Malaria parasite infection cycle is shown in Figure 1. The parasites persuade separation of the infected hepatocyte takes place, enabling it to relocate to the liver sinusoid where sprouting of parasite-filled vesicles called merosomes (Figure 1: Label 1). The new merozoites rapidly divide within erythrocytes, sometimes synchronously in cycles with fever and chills (Figure 1: Label 2). Responding to an unconfirmed cue, few parasites separate into male and female gametocytes (Figure 1: Label 3), which are the forms that live inactively in the circulatory system for a week. When gametocyte enters the mosquito by means of blood, they quickly transit to become initiated male and female gametes (Figure 1: Label 4). The motile and fleeting diploid parasite frame, the ookinete, moves out of the blood (Figure 1: Label 5), over the peritrophic lattice to the mid-gut partition where an oocyst is shaped (Figure 1: Label 6). After a meiotic decrease in the chromosome, numbers of sporozoites are framed inside the oocyst (Figure 1: Label 7). Finally, the oocyst splits and sporozoites relocate to the salivary organ to anticipate exchange with vertebrate host. Malaria infection can grow to hypoglycemia, cerebral malaria or anemia as result the blood carrying capillaries are blocked due to the thickness of blood. This happens, when the parasite is drug resistant or there is a lack of availability in proper medicines. The cerebral malaria is a key factor of lifetime learning disabilities (11), it can cause coma and further may lead to death.

Figure 1

Malaria parasite life cycle.

There have been few studies in the field of regional classification of malaria-proneness. Recently, Santosh et al. (12) presented an artificial neural network (ANN) that uses a sigmoid function as an activation function in a prediction model for malaria using data engineering, for the southern regions of India, and addressed the problems of scalability and time complexity for traditional machine learning algorithms. For classification, a feedforward multilayer perceptron (MLP) is a widely used neuron model that uses backpropagation (BP) as a learning method with weight updating (13). Feedforward nets are efficient in terms of classification, but are time-consuming.

The first artificial neuron was proposed by McCulloch et al. (14), and was known as a linear threshold unit (LTU) or threshold logic unit (TLU). This model was a mathematical interpretation of a neuron. Sharp et al. (15) proposed an electrical model of an artificial neuron and explained the oscillation properties exhibited by this neuron. Izhikevich (16) introduced the concept of resonate and firing in neural activities, which exhibited biological properties. In recent years, spiking neuron models (third-generation ANNs) are used in classification and prediction problems and these have shown improvements over conventional neuron models. Abbott (17) introduced the concept of threshold values for spike generation, and stated that an integrate-and-fire neuron (IFN) exhibits almost all of the characteristics of pharmacological input neurons or natural neurons. Abbott et al. (18) developed a generalized non-linear IFN for electrical circuits. Stein et al. (19) developed a new model by introducing a leaky term into the IFN and it was added to the membrane potential, the model was termed a leaky IFN (LIFN), and this model was developed as a special case of the generalized IFM model. Nicolas et al. (20) proposed another special case called the quadratic IFN (QIFN), which was derived from the generalized IFN model. Fourcaud et al. (21) presented an exponential IFN (EIFN) neuron model which uses an input current in the form of an exponential or spiking current. A further description of spiking neural networks is presented in the Discussion section.

The interspike interval (ISI) is a key factor affecting the passing of information from one neuron to another. Yadav et al. (13) observed that ANNs are efficient in performing pattern classification when the biological properties of the neuron are included. The authors used an MLP with an ISI derived from IFN, and this yielded better accuracy and lower time complexity. We thus hypothesize that the ISI obtained from the proposed neuron model with a BPNN will yield a regional classifier for our objective solution. This neuron mimics the characteristics of natural neurons, and when embedded with BPNN, can provide a more robust solution. This study presents an ISI-based BPNN (ISI-BPNN) function in which the proposed architecture is a single-pass spiking learning strategy which is useful for nonlinear regression tasks.

Based on this approach, we implemented an architecture that leads to the following contributions: (i) the design of a new spiking function for a nonlinear IFN model (NLIFN) and its ISI; (ii) this ISI is then used as an aggregation function in a BNN, and is referred to as an ISI-BPNN. In addition, weight updating equations are derived and comparative studies are performed via experiment; (iii) real-world malaria data are used in a comparative performance evaluation of the proposed ISI-BPNN. The rest of the paper is organized as follows: Section 3 presents a mathematical model of the spiking function along with the ISI-BPNN architecture. Machine learning architecture is discussed in Section 4, and the experimental protocol involving a real-world malaria dataset is discussed in Section 5. The results of experiments using the proposed method are presented in Section 6. A performance evaluation of ISI-BPNN is conducted in Section 7, and Section 8 contains a scientific validation and statistical analysis. In Section 9, we summarize the paper and present a discussion of the proposed design, including benchmarking, special notes on the sigmoid function and neural networks and their strengths and weaknesses, and future work. Finally, the study is concluded in Section 10.

The design of the proposed system is inspired by the generalized IFN model. We create a spiking function, and the aggregated output is used as input to the sigmoid function in the BP algorithm. We therefore call our approach ISI-BPNN. The global model is shown in Figure 2. The object process diagram illustrates the procedure of the model development, and contains three processes: the design of the spiking function, the design of the ISI and the integration of ISI with BPNN, as shown by three different ellipses. The spiking model (phase I) is designed using the generalized IFN model, retaining features such as robustness, constraints and a single-pass model (derived in the next section). Phase II uses constraints and an integration model to generate the aggregate function. Phase III uses this weighted aggregated sum to generate the ISI-BPNN, using the steepest descent paradigm. Following this, the input to the sigmoid function is constructed, and finally, the new classifier is obtained from the sigmoid function.

Figure 2

Object process model showing the aggregate sum used in the design of ISI-BPNN

An IFN is an efficient model that is capable of computing the characteristics of a biological neuron over time. In an IFN, the membrane potential is directly proportional to an externally injected current. When this current is injected, the voltage potential rises, and after a certain time settles down to a threshold value. Once the membrane potential reaches a desired threshold, it generates a spike and then resets the voltage immediately after this spike. Usually, a biological neuron has memory, an equivalence term called a leak term is added to an IFN, and this is known as an LIFN. This is the most representative model of an actual biological neuron. The memory term is included in an action potential that represents the diffusion of ions inside the membrane cell. Biologically, diffusion occurs when the membrane cell is in disequilibrium. The generalized nonlinear IFN (21) is represented as:

(1)

where is the membrane constant, is the membrane potential at time , is the leaky inductance of the membrane, represents the external input current, and and represent the generalized functions of the membrane potential. We can obtain the QIFN by substituting a specific function of second order () for the membrane potential () and can obtain the EIFN model by substituting an exponent term () as a specific function for the membrane potential in the general equation for a nonlinear IFN, as shown in Eq. (1).

A new spiking model for a nonlinear IFN is proposed in this work. The proposed spiking function has the form, where and are positive numerals and have the relation: ;, represents the membrane potential, and represent the membrane potential function at time t and the cubic power function of respectively and is the inductance of the membrane. If we substitute the values of and into Eq. (1), i.e. and the values of a and b are substituted, we obtain an updated equation as given in Eq. (2):

(2)

where, , , , , and are the membrane potential, leak potential, external current input, time, membrane time constant, and incremental time, respectively (the derivation of Eq. (2) is given in the Supplementary Material). Once the potential reaches a threshold voltage , the membrane potential dynamics are interspersed, and then the potential is then reset after the spike following re-initialization at a resting potential of . The term is obtained by comparing the RHS of Eq. (1) and Eq. (2). The response from the firing rate of the proposed neuron model is analytically calculated as the first derivative of , i.e.

(3)

It is observed that the derivative satisfies the properties of nonlinear neuron model, i.e.

(4)

Furthermore, this nonlinear model also meets the requirements of the condition of the spike sloping factor ), i.e.

(5)

The proposed function has a lower order than the other spiking functions including , and proposed earlier. Biologically plausible spikes are generated by this proposed function. The LHS of Eq. (1) can be denoted by , a nonlinear potential of the membrane, and this is given as:

(6)

where, ; is a constant; * is the multiplier; R is the resistance of the membrane; and

is the external input current (and thus the product of the latter two parameters represents the voltage). The spikes obtained using a two-dimensional function from Eq. (2), at are shown in Figure 3(b). Izhikevich (23) stated that function should satisfy non-degeneracy and transversality conditions. Eq. (5) shows that a partial derivative of with respect to is not equal to zero. Hence, also satisfies the property of transversality and satisfies the property of non-degeneracy, since the second derivative is not equal to zero.

Figure 3

Spike dynamics (a) IFN, (b) ISI-BPNN

The ISI is the main source of information exchange, and appears in mammalian neurons (19). The zero-order solution of Eq. (2) gives an ISI that is responsible for inter-neuron communication. The ISI in the proposed model is given by Eq. (7) as follows:

(7)

where

The firing rate or frequency () is the inverse of time, and thus :

(8)

The spike generation timing is also known as the ISI, as shown in Eq. (7), and is necessary for the biological neurons to communicate. The integrated solution is given in Eq. (7) and is used in the construction of the aggregation function, which is also used as an activation function in the ISI-BPNN. The firing pattern exhibited by the model is shown in Figure 3. We can see from Figure 3(a) and Figure 3(b) that the spikes generated by IFN and ISI-BPNN are similar. The key benefit of the proposed spike-generating function is that it contains lower-order terms than other existing quadratic and exponential neuron models. The spike-generating function also reduces the computational complexity.

The new aggregation function for ISI-BPNN is derived from Eq. (2), as explained in this section. A parametric representation of Eq. (7) can be given as:

(9)

where,

The computation of the new ISI-based BNN is shown in Figure 4. The network diagram consists of two layers. Input has attributes, and the first layer has neurons for each distinct feature and the second layer is the output layer.

Figure 4

Computation of the aggregated weight function

Assuming that the input to the neuron is and that the corresponding weights are , and that input neurons are connected to the neuron in the second layer, then the weighted aggregate at neuron is defined as:

(10)

where is the number of input neurons.

This subsection explains the development of ISI-BPNN and the training algorithm used. After the construction of the proposed network is explained, the training algorithm is given based on the steepest-descent formulae.

The developed model was tested against conventional classifiers such as MLP, IFN, SVM, and GCNN. The theoretical concepts underlying these classifiers are discussed in this section.

4.1.1. Multilayer perceptron

The MLP is a feedforward artificial neural network (36) with a minimum of three layers: the first is an input layer, the second a hidden layer and last an output layer. The activation function is used at each layer in the MLP except the input layer. Figure 6(a) shows the traditional model of a feedforward network.

Figure 6

Conventional classifiers (a) MLP, (b) SVM and (c) GCNN

The malaria data used in this study were collected from a government infectious disease (ID) hospital in Ponda, Goa, via a Department of Science and Technology (DST), Science and Engineering Research Board (SERB) project at the National Institute of Technology Goa, India.

The motive for performing this experiment was to examine the effects of changes in accuracy by making changes in the training set using the K2 and K10 protocols. Each fold is a subset of our dataset; K2 means that the data were divided in to two parts in a 1:1 ratio, i.e. half (50%) for training and half (50%) for testing, whereas K10 means that the data were divided into 10 parts in a 9:1 ratio, meaning that 90% of data were used for training and 10% for testing. These experiments were repeated 10 times on a random basis, and the average accuracy was recorded.

The motive for this experiment was to identify changes in performance by making changes to the dataset sample size. The sample size played a vital role in both performance and computation time during every iteration. We used four sizes of datasets: monthly, half-yearly, yearly and combined three-year data, and these were termed CN1, CN2, CN3 and CN4 which contains 129, 774 , 1548 and 4744 records respectively. It was therefore necessary to run K-fold protocols for these four types of datasets.

The aim of this experiment was to perform a comparative analysis of ISI-BPNN and calculate the mean accuracies for other classifiers including SVM, IFN, GSNN and MLP.

The system accuracy () can be expressed using the following function:

(12)

where, is the partition protocol i.e. K2 or K10. are the number of trials, the index of the trial number, the dataset index and the size of the data respectively, as shown in Figure 7, in the figure, numbers on the bar are standard deviation. The accuracies for the K2 and K10 protocols are represented in green and purple, respectively. As can be seen from the figure, the proposed ISI-BPNN scheme exhibited an average accuracy for K2 and K10 of 91.78% and 98.06%, respectively. Figure 8, Figure 9 and Figure 10 show the monthly prediction results of our proposed algorithm for the years 2014, 2015 and 2016 respectively. We can observe from these Figures that our proposed algorithm gives the desired results, as the predictions are much closer to the actual cases. Here, we can clearly observe that for consecutive years, our model gave very accurate predictions and performed well, requiring less computational time.

Figure 7

Prediction results of ISI-BPNN for K-fold protocols (numbers on the bar are standard deviation)

Figure 8

Actual vs. predicted malaria patients for the year 2014

Figure 9

Actual vs. predicted malaria patients for the year 2015

Figure 10

. Actual vs. predicted malaria patients for the year 2016

To identify the effects of the size of the training data on the ISI-BPNN and other classifiers, we carried out an experiment by varying the size of the dataset. We conducted experiments on datasets of different sizes, i.e. CN1, CN2, CN3 and CN4. The accuracies for the K10 protocol obtained from these varying data are shown in Figure 11 (a) and the corresponding data are given in Table 3 where we can conclude that as the size of the dataset increased, the model became well-trained, and outperformed the other methods by yielding accuracies of 98.67% for DC3 and 99.28% for the DC4 dataset.

Figure 11

Prediction accuracies for (a) different data sizes (b) different classifiers (numbers on the bar are standard deviation)

The results for different classifiers are shown in Figure 11 (b), and the corresponding data are given in Table 3. The SVM, IFN, MLP, GCNN, and ISI-BPNN classifiers exhibited average accuracies of 85.14%, 89.8%, 87.6%, 87.0% and 91.78%, respectively, for the K2 protocol. Prediction results for the K2 and K10 protocols are listed in Table 4 respectively. In Table 5, the average improvement in accuracy for the K2 and K10 protocols is compared with the ISI-BPNN scheme; the ISI-BPNN model exhibited an 11.9% increase in accuracy over the conventional classifiers.

The receiver operating characteristic (ROC) curve is used to validate the diagnostic capability of the ISI-BPNN classifier. The plotted curve and corresponding area under the curve (AUC) is shown in Figure 12. The AUC is 0.9636 for ISI-BPNN, implying a 96% chance (or probability of 0.96) that model classifies the data correctly. Thus, the system is accurate enough to perform binary classification. Conventional classifiers have a lower AUC(s).

Figure 12

ROC curve and corresponding area under the curve

In the sample dataset, as shown in Table 6, there are four features to be input to the system and one response variable. The ground truth variables are the maximum and minimum temperatures, rainfall, and humidity. The threshold for maximum temperature is 40 ± 2.5°C, for minimum temperature 15 ± 2.5°C, for rainfall 120 ± 5 mm and for humidity 40 ± 5%. The accuracies are recorded in order to analyze the sensitivity to the variables, and are shown in Figure 13, Figure 14, Figure 15, and Figure 16, for the four variables. It is clear from the standard deviation shown in Table 7 that the model has low sensitivity (< 5%) to all parameters. Other classifiers also have low sensitivity; however, ISI-BPNN is more robust than IFN, MLP, SVM and GCNN.

Figure 13

Sensitivities to minimum temperature

Figure 14

Sensitivities to maximum temperature

Figure 15

Sensitivities to rainfall

Figure 16

Sensitivities to humidity

Segregation analysis is performed in order to statistically validate the developed model. The index of dissimilarity, segregation, exposures and odds ratio/risk ratio are calculated. These as given as follows:

The Thoracic Surgery dataset is multivariate, and the attributes are an integer and a real number. The data are from primary lung cancer patients who underwent major lung resections. The dataset is related to patients who survived for one or more years after the operation, and life expectancy is used as the class in the dataset. Patients who survived for one or more years are classified as True (T) and patients who did not survive as False (N).

The Parkinson’s dataset contains values of biomedical voice measurements of patients with Parkinson’s disease. There are 23 records of Parkinson’s disease among the 31 records in the dataset. The dataset consists of 23 attributes and 197 instances, and is a multivariate dataset containing real attributes that are specifically used for classification problems. This was originally used in a feature selection method for voice disorders using recorded speech signals. The improvement shown by ISI-BPNN in comparison with conventional classifiers on the Thoracic Surgery and Parkinson’s datasets for the K2 and K10 protocols is given in Table 9. The comparative analysis of validation datasets is shown in Figure 17.

Figure 17

Validation accuracy for the dataset (a) Thoracic Surgery and (b) Parkinson’s

Experiments were carried out to test the robustness and validate the proposed method using the Thoracic Surgery and Parkinson’s datasets. The average accuracy for the Thoracic Surgery dataset for the K2 protocol was found to be 0.7588, and for the K10 protocol, this was 0.8236; the average accuracy for the Parkinson’s dataset for the K2 protocol was found to be 0.8175, and for the K10 protocol, this was 0.8818, as represented below in Figure 18.

Figure 18

Bar representation of accuracies for two datasets (numbers on the bar are standard deviation)

The improvements offered by ISI-BPNN over conventional classifiers on these two datasets were found to be 11.4% for the Thoracic Surgery dataset and 6.05% for the Parkinson’s dataset. These values show that if the size of the training dataset is increased, the accuracy of the system is high compared with the smaller dataset provided during execution. In the K2 protocol, half of the data are used for training and the other half for testing, giving lower accuracy for both datasets compared to the K10 protocol, where 90% of the data are used for training and 10% for testing. Thus, the proposed method is validated against two datasets.

We trained ISI-BPNN using the training data, and then used the testing data to generate predictions. This process was repeated with the MLP, GCNN, SVM, and IFN schemes. Table 9 shows the results using the proposed method and other existing methods such as MLP, IFN, SVM and GCNN using K10. We can easily see that ISI-BPNN outperforms the other models, giving accuracies of 89.93%, 98.67%, and 99.28% for monthly and yearly data and historical data over three years.

To validate the proposed methodology, we performed these tests on two more datasets called Parkinson's and Thoraric Surgery, retrieved from the UCI Repository. The results are shown in Table 10. After every round, the dataset was shuffled and the protocol was applied. The results obtained from these validation experiments shows that the accuracy of ISI-BPNN increases as the size of the training dataset.

The benchmarking study and comparative analysis is shown in chronological order, and the attributes are depicted in the columns of the Table 11. As can be observe, none of the previous studies applied the CV protocol (see column C6). Although many authors have worked on malaria disease, most have used laboratory data from blood samples, and have classified malaria-infected and healthy cells. These authors have also included malaria parasite and species data to identify the disease in their study. In some of these studies, metrological data were also used. In the present study, we focused on metrological data and obtained malaria data from a regional government hospital. Scientific validation is given in column C7. The proposed method exhibits better accuracy than other methods (see column C9). As completely different approaches have been developed over the last ten years, many experiments have been carried out to achieve the present state of the art. However, despite the large variety of studies, current performance is not satisfactory for clinical use. Several prior articles simply discuss the sensitivity and specificity of classification, representing just one in operation purpose on a receiver in operation characteristic. Some publications aim to identify the progress and control status, which may add an additional complete analysis of any technique for various sensitivity necessities.

Austeclino et al. (33) used immunological and epidemiological data and the past infection history of the patient for malaria diagnosis. An ANN and a Bayesian network (BN) were used with a comparison of the classification (diagnosis) results using light microscopy and polymerase chain reaction (PCR) laboratory methods. The authors reported improvements in accuracy of 18.75% and 6.25% for the ANN and BN, respectively, compared with microscopic laboratory tests. Memeu et al. (36) developed a method of identification of parasite life stages using blood smear images. These authors used an ANN for the classification of infected erythrocytes in different stages of the parasite life cycle. The results showed an accuracy of identification of species of the plasmodium family of 96.3%. Chavan et al. (35) used image processing method for malaria screening involving an SVM-based classifier, and used a gray-level co-occurrence matrix to extract features from RBC colored images (later converted to grayscale) which were used as input to an SVM and an ANN. These authors obtained classification accuracies of 98.25% and 78.53% for SVM and ANN, respectively. Chiroma et al. (24) discussed the classification of malaria using thin and thick blood smears with a Jordan-Elman neural network (recurrent neural network). These authors achieved a classification accuracy of 96.4%. Vijeta et al. (34) performed prediction of malaria outbreaks using SVM and ANN as data mining classifiers, and obtained RMSE values of 0.12 and 0.47, respectively, i.e. with superior performance from the SVM. Purnima et al. (31) developed an ANN-based classifier for the binary classification of red blood cells (infected vs. normal) taken from holographic images. This system was trained on the quantitative features derived from the cellular images, and demonstrated an accuracy of 90%. Rahila et al. (30) used the patient’s medical history (including shivering, vomiting, dry cough, back pain and headache) and symptoms as input to the MLP and performed malaria classification as positive or negative. Authors used backpropagation, backpropagation with momentum and a resilient propagation learning rule for MLP training, and obtained a classification accuracy of 85% in the backpropagation learning method. Santosh et al. (12) presented an ANN that used a sigmoid function as an activation function for a prediction model for the prevalence of malaria using big data processing; this was applied to the southern regions of India, and addressed the problem of scalability and time complexity for traditional machine learning algorithms. Belay (32) applied a support vector regression (SVR) and ANN in the classification of malaria data. The results showed values of root mean square error (RMSE) of 4.29 and 5.57, respectively, for the SVR and ANN. In the proposed method, we develop a new spiking function and obtain the ISI, which is aggregated and used as an input to the sigmoid function. The new sigmoid function is also used as an activation function in the backpropagation algorithm, and is called ISI-BPNN. Metrological factors are used as input data for the number of malaria cases within a particular geographical location. We also used K2 and K10 K-fold validation protocols, where we achieved accuracies of 88.09% and 98.67% for K2 and K10, respectively.

The sigmoid function is an activation function used in a backpropagation algorithm. As the input to the conventional sigmoid function is replaced in the proposed model with an aggregated spiking function, the protocol is known as ISI-based backpropagation. The new sigmoid function plays a vital role in the system dynamics. Once all these analytical studies performed, we can see from many points that, the developed model is obtaining its goal. The hypothetical system used for the development of this model was evaluated using several measures, and those measures were justified by tests and analysis. One of the major factors in the success of the developed model is a lower computational complexity due to the spiking sigmoid function. The developed model has a lower order of computational function, making it faster and less complex than legacy models. Another factor is the use of K-fold protocols for shuffling the data during computation, which makes the system more reliable in terms of the different sizes of data inputs. We observed that for a smaller training dataset, the prediction accuracy was poor, but when of the size of the test dataset was increased, the accuracy increased. For the larger training set, the accuracy was about 99.28%, as shown in the benchmarking study. The learning behavior of the system is similar to the natural neurons in the human brain; as more knowledge is obtained, performance increases. In the same way, the accuracy of the proposed method is higher for larger training samples. From this point of view, we have achieved our goal of learning and developing a biological neuron model.

Neural networks are often used for classification and regression tasks. Feng et al. (25,26) discussed IFN models with current inputs as part of a development series of third-generation neural networks. A neuron model was trained and applied to a binary classification (zero or one) equivalent to XOR gates. A spike was generated if the voltage was greater than a threshold when the current was applied to an R-C circuit. Mishra et al. (22) applied single and quadratic IFN models to the binary classification of an XOR problem (similar to Feng et al. (25)) and to a linear regression-based classification. Wulfram et al. (27) showed mathematically that a single neuron can be used for classification, and referred to this as the plasticity phenomenon. Chandra et al. (28) presented a new neuron model similar to quadratic IFN for classification, using a lower-order activation function, and demonstrated a reduction in computational complexity. Schollas et al. (29) presented the modeling of bio-inspired neural network and applied to time domain beamforming, model was used for sonar ranging, focusing and steering. This work was influenced by two contributions from Yadav et al. (13) and Chandra et al. (28), in which a new spiking model was presented by the authors.

It can be observed that ISI-BPNN performs better in terms of prediction. Due to the use of a lower-order function, the model has a lower computational complexity than traditional models; as the size of the dataset and the number of iterations are increased, the proposed model performs faster, and the time complexity is lower. The scope of this experiment is limited to numerical types of data, and a limited amount of collected data is used. We can use this system for a broad range of datasets, and can extend the model to add more features such as the ratio of vegetation, water index, geographical area and population index to achieve better results. This model can also be applied to different datasets for the early prediction of disease. Based on our results, it is evident that our model can be used as an early predictor for malaria for epidemic-prone regions. We could also adapt fuzzy rule sets to give more robustness to the system. Through this study, we can serve public epidemic healthcare and prevent the spread of life-threatening diseases by the early prediction of disease. In future, this work will be extended to help ensure the goal of zero cases of malaria.