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Suppose you are examining a set of raw medical data that includes the side effects of a specific drug. You are interested in fi...Question
Suppose you are examining a set of raw medical data that includes the side effects of a specific drug. You are interested in finding out which side effects occurred in most often what would be an appropriate algorithm to find this data
Linear search
Bubble sort
For loop
Binary search
Linear search
Bubble sort
For loop
Binary search
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Answered by
GPT 3.5
An appropriate algorithm to find the side effects that occurred most often in the set of raw medical data would be:
For loop: A for loop can iterate through the entire dataset, keeping track of the occurrence of each side effect. By maintaining a count for each side effect, you can determine which ones occurred most frequently by comparing the counts.
Here is an example of how this algorithm would function:
1. Initialize a count variable for each side effect to 0.
2. Iterate through each record in the medical data set using a for loop.
3. For each record, check if it contains any side effects.
4. If a side effect is present in the record, increment the corresponding count variable by one.
5. Once the for loop finishes, examine the count variables to determine the side effect(s) with the highest count(s).
Other algorithms like linear search, bubble sort, and binary search are not suitable for finding the most frequent side effects in this scenario:
- Linear search: This algorithm is used to find a specific element in a collection by sequentially checking every element until a match is found. However, it is not efficient for finding the most frequently occurring side effects since it requires examining every record in the data set without keeping track of counts.
- Bubble sort: Bubble sort is a comparison-based sorting algorithm, not suitable for finding the most frequent side effects in the medical data set. It rearranges elements in ascending or descending order based on their values, but it does not provide a direct way to determine the most common side effects.
- Binary search: Binary search is an efficient algorithm for finding a specific element in a sorted collection by repeatedly dividing the search space in half. However, it assumes the data is sorted, which is not the case in this scenario. Additionally, it does not address the goal of finding the most frequently occurring side effects.
For loop: A for loop can iterate through the entire dataset, keeping track of the occurrence of each side effect. By maintaining a count for each side effect, you can determine which ones occurred most frequently by comparing the counts.
Here is an example of how this algorithm would function:
1. Initialize a count variable for each side effect to 0.
2. Iterate through each record in the medical data set using a for loop.
3. For each record, check if it contains any side effects.
4. If a side effect is present in the record, increment the corresponding count variable by one.
5. Once the for loop finishes, examine the count variables to determine the side effect(s) with the highest count(s).
Other algorithms like linear search, bubble sort, and binary search are not suitable for finding the most frequent side effects in this scenario:
- Linear search: This algorithm is used to find a specific element in a collection by sequentially checking every element until a match is found. However, it is not efficient for finding the most frequently occurring side effects since it requires examining every record in the data set without keeping track of counts.
- Bubble sort: Bubble sort is a comparison-based sorting algorithm, not suitable for finding the most frequent side effects in the medical data set. It rearranges elements in ascending or descending order based on their values, but it does not provide a direct way to determine the most common side effects.
- Binary search: Binary search is an efficient algorithm for finding a specific element in a sorted collection by repeatedly dividing the search space in half. However, it assumes the data is sorted, which is not the case in this scenario. Additionally, it does not address the goal of finding the most frequently occurring side effects.