Alzheimer Disease Prediction Using Python Machine Learning

Features: -
Alzheimer's is a type of dementia that causes problems with memory, thinking and behaviour. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. Dementia is not a specific disease. It’s an overall term that describes a group of symptoms associated with a decline in memory or other thinking skills severe enough to reduce a person’s ability to perform everyday activities. Alzheimer’s disease accounts for 60 to 80 percent of cases. Vascular dementia, which occurs after a stroke, is the second most common dementia type. But there are many other conditions that can cause symptoms of dementia, including some that are reversible, such as thyroid problems and vitamin deficiencies. Dementia is a general term for loss of memory and other mental abilities severe enough to interfere with daily life. It is caused by physical changes in the brain. Alzheimer’s is the most common type of dementia, but there are many kinds. The input data is taken from the dataset repository. In our process, we are take the Alzheimer’s disease dataset as input. The system is developed the machine learning algorithm such as Support vector machine and logistic regression. The results shows that the performances metrics such as accuracy, sensitivity and specificity.
PROPOSED SYSTEM:
In this system, the Alzheimer’s dataset was taken as input. The input data was taken from the dataset repository. Then, we have to implement the data preprocessing step.in this step, we have to handle the missing values for avoid wrong prediction. If there is present any missing values in our input data, we have to replace the missing values by zero or Nan values. Then, we have to use label encoding, to encode the label for input data. To encode the columns into numeric values. Next, we have to implement the data splitting. In this step, we have to split the data into test and train. Then, we have to implement the machine learning algorithms such as Support Vector Machine (SVM) and Logistic regression (LR). Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall, sensitivity and confusion matrix.
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