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Epileptic Seizures Prediction

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Epileptic Seizures Prediction

Case Study - Epileptic Seizures Prediction

In this study, a critical disease is selected, Epileptic Seizures. Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication.

our machine learning model is applied to the Epileptic Seizures dataset to predict the risk of this disease in an individual. An end-to end process is used where people must enter their details in the web application and submit the data. The real-time processing takes place, and the risk is predicted within a few seconds.

The web application that is used as a real-time database on the cloud is the cloud-native database. The trained parameters of the model are stored in the database, and prediction is done in real-time.

Further, the user is also notified of the accuracy of the model. Apart from this, the news article from trusted sources is also shared in the app in real-time.

As in all disease prediction models, patient data will be preprocessed first. The second step will be the first step that defines the prediction model. Many parameters and hyperparameters must be set when defining the model. These elements have a very significant effect on accuracy, they can also prevent under-fitting and overfitting of our prediction model. The third step is to fit the data to the model and finally the fourth step will be to verify the model accuracy.

Challanges

preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate.

How we predict the Epileptic Seizures ?

Prediction of epileptic seizures is considered by classifying a binary class that classifies test data into either preictal state or ictal state. On average, the prediction sensitivity after applying this algorithm is 73.90%.

Detecting the appearance of preictal state predicts the seizure. Therefore, the purpose of our investigation is to detect the appearance of preictal state for epileptic seizures. Machine learning models are used to predict epileptic seizures.

Our machine learning model include;

  • EEG signal acquisition
  • signal preprocessing
  • features extraction from the signals
  • and finally classification between different seizure states

Data

The first part is about preparing and preprocessing the data. This part discusses different features related to each other and also how some features are eliminated from the process.

Heart Disease Data

The load data consists of 70,000 data points.Out of the features listed in the table, the features used include “age,” “gender,” “height,” “weight,” “cholesterol,” “gluc,” “smoke,” “alco,” “ap_hi,” and “ap_lo.”. There were some outliers. *e value of systolic blood pressure above 200 and the value of diastolic pressure above 150 are referred to as outliers here. A snapshot of the dataset is shown below.

The dataset can be seen as below. Dataset has demographic properies of the invidiual as wel as habits.

Heart disease dataset used in this study has features shown below.

Id

Age

Gender

Height

Weight

Ap_lo

Cholesterol

Gluc

Smoc

Alco

Active

Cardio

0

18393

2

168

62.0

110

80

1

1

0

1

0

1

20228

1

156

85.0

140

90

3

1

0

1

1

2

18857

1

165

64.0

130

70

3

1

0

0

1

3

17623

2

169

82.0

150

100

80

1

0

1

1

4

17474

2

158

58.0

100

60

80

1

0

0

0

For analysis of features, a heat map was drawn as below. According to the heat map, the most important features in determining heart disease include systolic and diastolic blood pressure, cholesterol, and age.


Features in heart disease dataset

The most important features in determining heart disease shown in the table as below.

Feature

Description

Age

Days-integer

Height

Height in cm-integer

Weight

Weight in kg-float

Systolic blood pressure

Categorical code (1-women, 2-men)

Diastolic blood pressure

Integer

Cholesterol

1: normal, 2: above normal, 3: well above normal

Glucose

1: normal, 2: above normal, 3: well above normal

Alcohol intake

Binary

Physical activity

Binary

Presence or absence of cardiovascular disease

Binary

Implementation
Implementation for Forecasting

After cleaning and analyzing the dataset, machine learning models were applied. The logistic regression model is used for all the datasets. To make the prediction, the coefficients and intercept of all the three logistic regression models are stored in a cloud-native real-time database.

Results
Below figure hows an example of prediction in our web app. From the comparative analysis, it is found that among the existing models, the proposed model outperforms the competitive models in terms of various performance measures.

Conclusion

This case study provides insights into using the machine learning models to predict the risk of heart disease in an individual based on answering a few questions related to various factors like travel history, age, gender, and blood pressure. Logistic regression is used for prediction.

The findings in this diagnosis service can be helpful in the early screening of potential heart disease patients. It can be helpful in the sense that the first screening can be performed at the comfort of home. If a high risk of disease is predicted in a patient, then it can be followed by clinical trials for confirmation.

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