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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.
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.
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;
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.
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.
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
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.
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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