A novel method of diagnosing cerebral infarction using a neural network, wherein plural sets of data previously obtained from healthy and sick persons, each including an age, measured values of coagulo-fibrinolytic molecular markers ( e.g., D-dimer, TAT and PAP) , an index indicative of the state of cerebral infarction (e.g., 0 for healthy persons and 1 for sick persons) and the like, are repeatedly input into a neural network to let it learn the correlation of these characteristics and, thereafter, a set of data of a person to be diagnosed, including his age, measured values of the coagulo-fibrinolytic molecular markers and the like, are input in the neural network to obtain an index indicative of his state of cerebral infarction as a degree of dangerousness of cerebral infarction. This method is significantly higher in accuracy as compared with the prior art methods using the same data.
Method of diagnosing cerebral infarction